Methods and materials for identifying the origin of a carcinoma of unknown primary origin

ABSTRACT

The present invention provides a method of identifying origin of a metastasis of unknown origin by obtaining a sample containing metastatic cells; measuring Biomarkers associated with at least two different carcinomas; combining the data from the Biomarkers into an algorithm where the algorithm normalizes the Biomarkers against a reference; and imposes a cut-off which optimizes sensitivity and specificity of each Biomarker, weights the prevalence of the carcinomas and selects a tissue of origin determining origin based on highest probability determined by the algorithm or determining that the carcinoma is not derived from a particular set of carcinomas; and optionally measuring Biomarkers specific for one or more additional different carcinoma, and repeating the steps for additional Biomarkers.

PARENT CASE TEXT

This application claims the benefit of U.S. provisional patent application Ser. Nos. 60/718,501 filed Sep. 19, 2005; and 60/725,680 filed Oct. 12, 2005.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

No government funds were used to make this invention.

REFERENCE TO SEQUENCE LISTING, OR A COMPUTER PROGRAM LISTING COMPACT DISK APPENDIX

Reference to a “Sequence Listing”, a table, or a computer program listing appendix submitted on a compact disc and an incorporation by reference of the material on the compact disc including duplicates and the files on each compact disc shall be specified.

BACKGROUND OF THE INVENTION

Carcinoma of unknown primary (CUP) is a set of heterogeneous, biopsy-confirmed malignancies wherein metastatic disease presents without an identifiable primary tumor site or tissue of origin (ToO). This problem represents approximately 3-5% of all cancers, making it the seventh most common malignancy. Ghosh et al. (2005); and Mintzer et al. (2004). The prognosis and therapeutic regimen of patients are dependent on the origin of the primary tumor, underscoring the need to identify the site of the primary tumor. Greco et al. (2004); Lembersky et al. (1996); and Schlag et al. (1994).

A variety of methods are currently used to resolve this problem. Several methods followed are diagrammed in FIGS. 1-2. Serum tumor Markers can be used for differential diagnosis. Although they lack adequate specificity, they can be used in combination with pathologic and clinical information. Ghosh et al. (2005). Immunohistochemical (IHC) methods can be used to identify tumor lineage but very few IHC Markers are 100% specific. Therefore, pathologists often use a panel of IHC Markers. Several studies have demonstrated accuracies of 66-88% using four to 14 IHC Markers. Brown et al. (1997); DeYoung et al. (2000); and Dennis et al. (2005a). More expensive diagnostic workups include imaging methods such as chest x-ray, computed tomographic (CT) scans, and positron emission tomographic (PET) scans. Each of these methods can identify the primary in 30 to 50% of cases. Ghosh et al. (2005); and Pavlidis et al. (2003). Despite these sophisticated technologies, the ability to resolve CUP cases is only 20-30% ante mortem. Pavlidis et al. (2003); and Varadhachary et al. (2004).

A promising new approach lies in the ability of genome-wide gene expression profiling to identify the origin of tumors. Ma et al. (2006); Dennis et al. (2005b); Su et al. (2001); Ramaswamy et al. (2001); Bloom et al. (2004); Giordano et al. (2001); and 20060094035. These studies demonstrated the feasibility of tissue of origin identification based on the gene expression profile. In order for these expression profiling technologies to be useful in the clinical setting, two major obstacles must be overcome. First, since gene expression profiling was conducted entirely on primary tissues, gene marker candidates must be validated on metastatic tissues to confirm that their tissue specific expression is preserved in metastasis. Second, the gene expression profiling technology must be able to utilize formalin-fixed, paraffin-embedded (FFPE) tissue, since fixed tissue samples are the standard material in current practice. Formalin fixation results in degradation of the RNA (Lewis et al. (2001); and Masuda et al. (1999)) so existing microarray protocols will not perform as reliably. Bibikova et al. (2004). Additionally, the profiling technology must be robust, reproducible, and easily accessible.

Quantitative RTPCR (qRTPCR) has been shown to generate reliable results from FFPE tissue. Abrahamsen et al. (2003); Specht et al. (2001); Godfrey et al. (2000); and Cronin et al. (2004). Therefore, a more practical approach would be to use a genome-wide method as a discovery tool and develop a diagnostic assay based on a more robust technology. Ramaswamy (2004). This paradigm, however, requires a smaller gene set to be developed. Oien and colleagues used serial analysis of gene expression (SAGE) to identify 61 tumor Markers from which they developed a RTPCR method based on eleven genes for five tumor types. Dennis et al. (2002). Another study which coupled SAGE and qRTPCR developed a panel of five genes for four tumor types and achieved an accuracy of 81%. Buckhaults et al. (2003). A more recent study coupled microarray profiling with qRTPCR, but used 79 Markers. Tothill et al. (2005).

SUMMARY OF THE INVENTION

The present invention provides a method of identifying origin of a metastasis of unknown origin by obtaining a sample containing metastatic cells; measuring Biomarkers associated with at least two different carcinomas; combining the data from the Biomarkers into an algorithm where the algorithm: normalizes the Biomarkers against a reference; and imposes a cut-off which optimizes sensitivity and specificity of each Biomarker, weights the prevalence of the carcinomas and selects a tissue of origin; determining origin based on highest probability determined by the algorithm or determining that the carcinoma is not derived from a particular set of carcinomas; and optionally measuring Biomarkers specific for one or more additional different carcinoma, and repeating steps as necessary for additional Biomarkers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1-2 depict prior art methods of identifying origin of a metastasis of unknown origin.

FIG. 3 depicts the present CUP diagnostic algorithm.

FIG. 4 depicts microarray data showing intensities of two genes in a panel of tissues. (A) Prostate stem cell antigen (PSCA). (B) Coagulation factor V (F5). The bar graphs show the intensity on the y-axis and the tissue on the x-axis. Panc Ca, pancreatic cancer; Panc N, normal pancreas.

FIG. 5 depicts electropherograms obtained from an Agilent Bioanalyzer. RNA was isolated from FFPE tissue using a three hour (A) or sixteen hour (B) proteinase K digestion. Sample C22 (red) was a one-year old block while sample C23 (blue) was a five-year old block. A size ladder is shown in green.

FIG. 6 depicts a comparison of Ct values obtained from three different qRTPCR methods: random hexamer priming in the reverse transcription followed by qPCR with the resulting cDNA (RH 2 step), gene-specific (reverse primer) priming in the reverse transcription followed by qPCR with the resulting cDNA (GSP 2 step), or gene-specific priming and qRTPCR in a one-step reaction (GSP 1 step). RNA from eleven samples was divided into the three methods and RNA levels for three genes were measured: β-actin (A), HUMSPB (B), and TTF (C). The median Ct value obtained with each method is indicated by the solid line.

FIG. 7 depicts CUP assay plate diagrams.

FIG. 8 is a series of graphs depicting the assay performance over a range of RNA concentrations.

FIG. 9 is an experimental workflow diagram: Marker candidate nomination and validation (9A); and assay optimization and prediction algorithm building and testing (9B).

FIG. 10 depicts expression of 10 selected tissue specific gene Marker candidates in FFPE metastatic carcinomas and prostate primary adenocarcinoma. For each plot the X axis represents the normalized Marker expression value.

FIG. 11 depicts assay optimization. (A and B) Electropherograms obtained from an Agilent Bioanalyzer. RNA was isolated from FFPE tissue using a three hour (A) or sixteen hour (B) proteinase K digestion. Sample C22 (red) was a one-year old block while sample C23 (blue) was a five-year old block. A size ladder is shown in green. (C and D) Comparison of Ct values obtained from three different qRTPCR methods: random hexamer priming in the reverse transcription followed by qPCR with the resulting cDNA (RH 2 step), gene-specific (reverse primer) priming in the reverse transcription followed by qPCR with the resulting cDNA (GSP 2 step), or gene-specific priming and qRTPCR in a one-step reaction (GSP 1 step). RNA from eleven samples was divided into the three methods and RNA levels for two genes were measured: β-actin (C), HUMSPB (D). The median Ct value obtained with each method is indicated by the solid line.

FIG. 12 is a heatmap showing the relative expression levels of the 10 Marker panel across 239 samples. Red indicates higher expression.

DETAILED DESCRIPTION

Identifying the primary site in patients with metastatic carcinoma of unknown primary (CUP) origin can enable the application of specific therapeutic regimens and may prolong survival. Marker candidates were then validated by reverse transcriptase polymerase chain reaction (RTPCR) on 205 FFPE metastatic carcinomas originating from these six tissues as well as metastases originating from other cancer types to determine specificity. A ten-gene signature was selected that predicted the tissue of origin of metastatic carcinomas for these six cancer types. Next, the RNA isolation and qRTPCR methods were optimized for these ten Markers, and applied the qRTPCR assay to a set of 260 metastatic tumors, generating an overall accuracy of 78%. Lastly, an independent set of 48 metastatic samples were tested. Importantly, thirty-seven samples in this set had either a known primary or initially presented as CUP but were subsequently resolved, and the assay demonstrated an accuracy of 78%.

A Biomarker is any indicia of the level of expression of an indicated Marker gene. The indicia can be direct or indirect and measure over- or under-expression of the gene given the physiologic parameters and in comparison to an internal control, normal tissue or another carcinoma. Biomarkers include, without limitation, nucleic acids (both over and under-expression and direct and indirect). Using nucleic acids as Biomarkers can include any method known in the art including, without limitation, measuring DNA amplification, RNA, micro RNA, loss of heterozygosity (LOH), single nucleotide polymorphisms (SNPs, Brookes (1999)), microsatellite DNA, DNA hypo- or hyper-methylation. Using proteins as Biomarkers includes any method known in the art including, without limitation, measuring amount, activity, modifications such as glycosylation, phosphorylation, ADP-ribosylation, ubiquitination, etc., or imunohistochemistry (IHC). Other Biomarkers include imaging, cell count and apoptosis Markers.

The indicated genes provided herein are those associated with a particular tumor or tissue type. A Marker gene may be associated with numerous cancer types but provided that the expression of the gene is sufficiently associated with one tumor or tissue type to be identified using the algorithm described herein to be specific for a particular origin, the gene can be used in the claimed invention to determine tissue of origin for a carcinoma of unknown primary origin (CUP). Numerous genes associated with one or more cancers are known in the art. The present invention provides preferred Marker genes and even more preferred Marker gene combinations. These are described herein in detail.

“Origin” as referred to in ‘tissue of origin’ means either the tissue type (lung, colon, etc.) or the histological type (adenocarcinoma, squamous cell carcinoma, etc.) depending on the particular medical circumstances and will be understood by anyone of skill in the art.

A Marker gene corresponds to the sequence designated by a SEQ ID NO when it contains that sequence. A gene segment or fragment corresponds to the sequence of such gene when it contains a portion of the referenced sequence or its complement sufficient to distinguish it as being the sequence of the gene. A gene expression product corresponds to such sequence when its RNA, mRNA, or cDNA hybridizes to the composition having such sequence (e.g. a probe) or, in the case of a peptide or protein, it is encoded by such MRNA. A segment or fragment of a gene expression product corresponds to the sequence of such gene or gene expression product when it contains a portion of the referenced gene expression product or its complement sufficient to distinguish it as being the sequence of the gene or gene expression product.

The inventive methods, compositions, articles, and kits of described and claimed in this specification include one or more Marker genes. “Marker” or “Marker gene” is used throughout this specification to refer to genes and gene expression products that correspond with any gene the over- or under-expression of which is associated with a tumor or tissue type. The preferred Marker genes are described in more detail in Table 1. TABLE 1 CUP panel SEQ ID Chip NO: Name designation sequence  1 SP-B 209810_at gaaaaaccagccactgctttacaggacaggg ggttgaagctgagccccgcctcacacccacc cccatgcactcaaagattggattttacagct acttgcaattcaaaattcagaagaataaaaa atgggaacatacagaactctaaaagatagac atcagaaattgttaagttaagctttttcaaa aaatcagcaattccccagcgtagtcaagggt ggacactgcacgctctggcatgatgggatgg cgaccgggcaagctttcttcctcgagatgct ctgctgcttgagagctattgctttgttaaga tataaaaaggggtttctttttgtctttctgt aaggtggacttccagattttgattgaaagtc ctagggtgattctatttctgctgtgatttat ctgctgaaagctcagctggggttgtgcaagc tagggacccattcctgtgtaatacaatgtct gcaccaatgct  2 TTF1 211024_s_at gtgattcaaatgggttttccacgctagggcg gggcacagattggagagggctctgtgctgac atggctctggactctaaagaccaaacttcac tctgggcacactctgccagcaaagaggactc gcttgtaaataccaggatttttttttttttt tgaagggaggacgggagctggggagaggaaa gagtcttcaacataacccacttgtcactgac acaaaggaagtgccccctccccggcaccctc tggccgcctaggctcagcggcgaccgccctc cgcgaaaatagtttgtttaatgtgaacttgt agctgtaaaacgctgtcaaaagttggactaa atgcctagtttttagtaatctgtacattttg ttgtaaaaagaaaaaccactcccagtcccca gcccttcacattttttatgggcattgacaaa tctgtgtatattatttggcagtttggtattt gcggcgtcagtctttttctgttgtaact  3 DSG3 205595_at ccatcccatagaagtccagcagacaggattt gttaagtgccagactttgtcaggaagtcaag gagcttctgctttgtccgcctctgggtctgt ccagccagctgtttccatccctgaccctctg cagcatggtaactatttagtaacggagactt actcggcttctggttccctcgtgcaaccttc cactgcaggctttgatccacttctcacacaa aatgtgatagtgacagaaagggtgatctgtc ccatttccagtgttcctggcaacctagctgg cccaacgcagctacgagggtcacatactatg ctctgtacagaggatccttgctcccgtctaa tatgaccagaatgagctggaataccacactg accaaatctggatctttggactaaagtattc aaaatagcatagcaaagctcactgtattggg ctaataatttggcacttattagcttctctca taaactgatcacgattataaattaaatgttt gggttcataccccaaaagcaatatgttgtca ctcctaattctcaagtac  4 HPT1 209847_at ctgcacccacctacttagatatttcatgtgc tatagacattagagagatttttcatttttcc atgacatttttcctctctgcaaatggcttag ctacttgtgtttttcccttttggggcaagac agactcattaaatattctgtacattttttct ttatcaaggagatatatcagtgttgtctcat agaactgcctggattccatttatgttttttc tgattccatcctgtgtccccttcatccttga ctcctttggtatttcactgaatttcaaacat ttgtc  5 PSCA 205319_at ttcctgaggcacatcctaacgcaagtttgac catgtatgtttgcaccccttttccccnaacc ctgaccttcccatgggccttttccaggattc cnaccnggcagatcagttttagtganacana tccgcntgcagatggcccctccaaccntttn tgttgntgtttccatggcccagcattttcca cccttaaccctgtgttcaggcacttnttccc ccaggaagccttccctgcccaccccatttat gaattgagccaggtttggtccgtggtgtccc ccgcacccagcaggggacaggcaatcaggag ggcccagtaaaggctgagatgaagtggactg agtagaactggaggacaagagttgacgtgag ttcctgggagtttccagagatg  6 F5 204713_s_at atcctctacagccagatgtcacagggatacg tctactttcacttggtgctggagaattcana agtcaagaacatgctaagcntaagggaccca aggtagaaagagatcaagcagcaaagcacag gttctcctggatgaaattactagcacataaa gttgggagacacctaagccaagacactggtt ctccttccggaatgaggccctgggaggacct tcctagccaagacactggttctccttccaga atgaggccctggaaggaccctcctagtgatc tgttactcttaaaacaaagtaactcatctaa gattttggttgggagatggcatttggcttct gagaaaggtagctatgaaataatccaagata ctgatgaagacacagctgttaacaattggct gatcagcccccagaatgcctcacgtgcttgg ggagaaagcacccctcttgccaacaagcctg gaaag  7 MGB1 206378_at gcagcagcctcaccatgaagttgctgatggt cctcatgctggcggccctctcccagcactgc tacgcaggctctggctgccccttattggaga atgtgatttccaagacaatcaatccacaagt gtctaagactgaatacaaagaacttcttcaa gagttcatagacgacaatgccactacaaatg ccatagatgaattgaaggaatgttttcttaa ccaaacggatgaaactctgagcaatgttgag gtgtttatgcaattaatatatgacagcagtc tttgtgatttattttaactttctgcaagacc tttggctcacagaactgcagggtatggtgag aaaccaactacggattgctgcaaaccacacc ttctctttcttatgtctttttact  8 PDEF 220192_x_at gagtggggcccttaaactggattcaaaaaat gctctaaacataggaatggttgaagaggtct tgcagtcttcagatgaaactaaatctctaga agaggcacaagaatggctaaagcaattcatc caagggccaccggaagtaattagagctttga aaaaatctgtttgttcaggcagagagctata tttggaggaagcattacagaacgaaagagat cttttaggaacagtttggggtgggcctgcaa atttagaggctattgctaagaaaggaaaatt taataaataattggtttttcgtgtggatgta ctccaagtaaagctccagtgactaatatgta taaatgttaaatgatattaaatatgaacatc agttaaaaaaaaaattctttaaggctactat taatatgcagacttacttttaatcatttgaa atctgaactcatttacctcatttcttgccaa ttactcccttgggtatttactgcgta  9 PSA 204582_s_at tggtgtaattttgtcctctctgtgtcctggg gaatactggccatgcctggagacatatcact caatttctctgaggacacagataggatgggg tgtctgtgttatttgtggggtacagagatga aagaggggtgggatccacactgagagagtgg agagtgacatgtgctggacactgtccatgaa gcactgagcagaagctggaggcacaacgcac cagacactcacagcaaggatggagctgaaaa cataacccactctgtcc 10 WT1 206067_s_at atagatgtacatacctccttgcacaaatgga ggggaattcattttcatcactgggagtgtcc ttagtgtataaaaaccatgctggtatatggc ttcaagttgtaaaaatgaaagtgactttaaa agaaaataggggatggtccaggatctccact gataagactgtttttaagtaacttaaggacc tttgggtctacaagtatatgtgaaaaaaatg agacttactgggtgaggaaatccattgttta aagatggtcgtgtgtgtgtgtgtgtgtgtgt gtgtgttgtgttgtgttttgttttttaaggg agggaatttattatttaccgttgcttgaaat tactgtgtaaatatatgtctgataatgattt gctctttgacaactaaaattaggactgtata agtactagatgcatcactgggtgttgatctt acaagat

The present invention provides a method of identifying origin of a metastasis of unknown origin by measuring Biomarkers associated with at least two different carcinomas in a sample containing metastatic cells; combining the data from the Biomarkers into an algorithm where the algorithm: normalizes the Biomarkers against a reference; and imposes a cut-off which optimizes sensitivity and specificity of each Biomarker, weights the prevalence of the carcinomas and selects a tissue of origin; determining origin based on highest probability determined by the algorithm or determining that the carcinoma is not derived from a particular set of carcinomas; and optionally measuring Biomarkers specific for one or more additional different carcinoma, and repeating steps as necessary for additional Biomarkers.

The present invention provides a method of identifying origin of a metastasis of unknown origin by obtaining a sample containing metastatic cells; measuring Biomarkers associated with at least two different carcinomas; combining the data from the Biomarkers into an algorithm where the algorithm i) normalizes the Biomarkers against a reference; and ii) imposes a cut-off which optimizes sensitivity and specificity of each Biomarker, weights the prevalence of the carcinomas and selects a tissue of origin; determining origin based on highest probability determined by the algorithm or determining that the carcinoma is not derived from a particular set of carcinomas; and optionally measuring Biomarkers specific for one or more additional different carcinoma, and repeating steps c) and d) for the additional Biomarkers.

In one embodiment, the Marker genes are selected from i) SP-B, TTF, DSG3, KRT6F, p73H, or SFTPC; ii) F5, PSCA, ITGB6, KLK10, CLDN18, TR10 or FKBP10; and/or iii) CDH17, CDX1 or FABP1. Preferably, the Marker genes are SP-B, TTF, DSG3, KRT6F, p73H, and/or SFTPC. More preferably, the Marker genes are SP-B, TTF and/or DSG3. The Marker genes may further include or be replaced by KRT6F, p73H, and/or SFTPC.

In one embodiment, the Marker genes are F5, PSCA, ITGB6, KLK10, CLDN18, TR10 and/or FKBP10. More preferably, the Marker genes are F5 and/or PSCA. Preferably, the Marker genes can include or be replaced by ITGB6, KLK10, CLDN18, TR10 and/or FKBP10.

In another embodiment, the Marker genes are CDH17, CDX1 and/or FABP1, preferably, CDH17. The Marker genes can further include or be replaced by CDX1 and/or FABP1.

In one embodiment, gene expression is measured using at least one of SEQ ID NOs: 11-58.

The present invention also encompasses methods that measure gene expression by obtaining and measuring the formation of at least one of the amplicons SEQ ID NOs: 14, 18, 22, 26, 30, 34, 38, 42, 46, 50, 54 and/or 58.

In one embodiment, the Marker genes can be selected from a gender specific Marker selected from at least one of: i) in the case of a male patient KLK3, KLK2, NGEP or NPY; or ii) in the case of a female patient PDEF, MGB, PIP, B305D, B726 or GABA-Pi; and/or WT1, PAX8, STAR or EMX2. Preferably, the Marker gene is KLK2 or KLK3. In this embodiment, the Marker genes can include or be replaced by NGEP and/or NPY. In one embodiment, the Marker genes are PDEF, MGB, PIP, B305D, B726 or GABA-Pi, preferably, PDEF and MGB. In this embodiment, the Marker genes can include or be replaced by PIP, B305D, B726 or GABA-Pi. In one embodiment, the Marker genes are WT1, PAX8, STAR or EMX2, preferably, WT1. In this embodiment, the Marker genes can include or be replaced by PAX8, STAR or EMX2.

The present invention provides methods of obtaining additional clinical information including the site of metastasis to determine the origin of the carcinoma; obtaining optimal biomarker sets for carcinomas comprising the steps of using metastases of know origin, determining Biomarkers therefor and comparing the Biomarkers to Biomarkers of metastases of unknown origin; providing direction of therapy by determining the origin of a metastasis of unknown origin and identifying the appropriate treatment therefor; and providing a prognosis by determining the origin of a metastasis of unknown origin and identifying the corresponding prognosis therefor.

The present invention further provides methods of finding Biomarkers by determining the expression level of a Marker gene in a particular metastasis, measuring a Biomarker for the Marker gene to determine expression thereof, analyzing the expression of the Marker gene according to any of the methods provided herein or known in the art and determining if the Marker gene is effectively specific for the tumor of origin.

The present invention further provides composition containing at least one isolated sequence selected from SEQ ID NOs: 11-58. The present invention further provides kits for conducting an assay according to the methods provided herein and further containing Biomarker detection reagents.

The present invention further provides microarrays or gene chips for performing the methods described herein.

The present invention further provides diagnostic/prognostic portfolios containing isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes as described herein where the combination is sufficient to measure or characterize gene expression in a biological sample having metastatic cells relative to cells from different carcinomas or normal tissue.

Any method described in the present invention can further include measuring expression of at least one gene constitutively expressed in the sample.

Preferably the Markers for pancreatic cancer are coagulation factor V (F5), prostate stem cell antigen (PSCA), integrin, β6 (ITGB6), kallikrein 10 (KLK10), claudin 18 (CLDN18), trio isoform (TR10), and hypothetical protein FLJ22041 similar to FK506 binding proteins (FKBP10). Preferably, Biomarkers for F5 and PSCA are measured together. Biomarkers for ITGB6, KLK10, CLDN18, TR10, and FKBP10 can be measured in addition to or in place of F5 and/or PSCA. F5 is described for instance by 20040076955; 20040005563; and WO2004031412. PSCA is described for instance by WO1998040403; 20030232350; and WO2004063355. ITGB6 is described for instance by WO2004018999; and 6339148. KLK10 is described for instance by WO2004077060; and 20030235820. CLDN18 is described for instance by WO2004063355; and WO2005005601. TR10 is described for instance by 20020055627. FKBP10 is described for instance by W02000055320.

Preferably the Marker genes for colon cancer are intestinal peptide-associated transporter HPT-1 (CDH17), caudal type homeo box transcription factor 1 (CDX1) and fatty acid binding protein 1 (FABP1). Preferably, a Biomarker for CDH17 is measured alone. Biomarkers for CDX1 and FABP1 can be measured in addition to, or in place of a Biomarker for CDH17. CDH17 is described for instance by Takamura et al. (2004); and W02004063355. CDX1 is described for instance by Pilozzi et al. (2004); 20050059008; and 20010029020. FABP1 is described for instance by Borchers et al. (1997); Chan et al. (1985); Chen et al. (1986); and Lowe et al. (1985).

Preferably the Marker genes for lung cancer are surfactant protein-B (SP-B), thyroid transcription factor (TTF), desmoglein 3 (DSG3), keratin 6 isoform 6F (KRT6F), p53-related gene (p73H), and surfactant protein C (SFTPC). Preferably, Biomarkers for SP-B, TTF and DSG3 are measured together. Biomarkers for KRT6F, p73H and SFTPC can be measured in addition to, or in place of any of the Biomarkers for SP-B, TTF and/or DSG3. SP-B is described for instance by Pilot-Mathias et al. (1989); 20030219760; and 20030232350. TTF is described for instance by Jones et al. (2005); US20040219575; WO1998056953; WO02002073204; 20030138793; and WO02004063355. DSG3 is described for instance by Wan et al. (2003); 20030232350; aWO2004030615; and WO2002101357. KRT6F is described for instance by Takahashi et al. (1995); 20040146862; and 20040219572. p73H is described for instance by Senoo et al. (1998); and 20030138793. SFTPC is described for instance by Glasser et al. (1988).

The Marker genes can be further selected from a gender specific Marker such as, in the case of a male patient KLK3, KLK2, NGEP or NPY; or in the case of a female patient PDEF, MGB, PIP, B305D, B726 or GABA-Pi; and/or WT1, PAX8, STAR or EMX2.

Preferably, the Marker genes for breast cancer are prostate derived epithelial factor (PDEF), mammaglobin (MG), prolactin-inducible protein (PIP), B305D, B726, and GABA-π. Preferably, Biomarkers for PDEF and MG are measured together. Biomarkers for PIP, B305D, B726 and GABA-Pi can be measured in addition to, or in place of Biomarkers for PDEF and/or MG. PDEF is described for instance by WO2004030615; WO2000006589; WO2001073032; Wallace et al. (2005); Feldman et al. (2003); and Oettgen et al. (2000). MG is described for instance by WO2004030615; 20030124128; Fleming et al (2000); Watson et al. (1996 and 1998); and 5668267. PIP is described for instance by Autiero et al. (2002); Clark et al. (1999); Myal et al. (1991) and Murphy et al. (1987). B305D, B726 and GABA-Pi are described by Reinholz et al. (2005). NGEP is described for instance by Bera et al. (2004).

Preferably the Markers for ovarian cancer are Wilm's tumor 1 (WT1), PAX8, steroidogenic acute regulatory protein (STAR) and EMX2. Preferably, Biomarkers for WT1 are measured. Biomarkers for STAR and EMX2 can be measured in addition to or in place of Biomarkers for WT1. WT1 is described for instance by 5350840; 6232073; 6225051; 20040005563; and Bentov et al. (2003). PAX8 is described for instance by 20050037010; Poleev et al. (1992); Di Palma et al. (2003); Marques et al. (2002); Cheung et al. (2003); Goldstein et al. (2002); Oji et al. (2003); Rauscher et al. (1993); Zapata-Benavides et al. (2002); and Dwight et al. (2003). STAR is described for instance by Gradi et al. (1995); and Kim et al. (2003). EMX2 is described for instance by Noonan et al. (2001).

Preferably the Markers for prostate cancer are KLK3, KLK2, NGEP and NPY. Preferably, Biomarkers for KLK3 are measured. Biomarkers for KLK2, NGEP and NPY can be measured in addition to or in place of KLK3. KLK2 and KLK3 are described for instance by Magklara et al. (2002). KLK2 is described for instance by 20030215835; and 5786148. KLK3 is described for instance by 6261766.

The method can also include obtaining additional clinical information including the site of metastasis to determine the origin of the carcinoma. A flow diagram is provided in FIG. 3. The invention further provides a method for obtaining optimal biomarker sets for carcinomas by using metastases of know origin, determining Biomarkers therefor and comparing the Biomarkers to Biomarkers of metastases of unknown origin.

The invention further provides a method for providing direction of therapy by determining the origin of a metastasis of unknown origin according to the methods described herein and identifying the appropriate treatment therefor.

The invention further provides a method for providing a prognosis by determining the origin of a metastasis of unknown origin according to the methods described herein and identifying the corresponding prognosis therefor.

The invention further provides a method for finding Biomarkers comprising determining the expression level of a Marker gene in a particular metastasis, measuring a Biomarker for the Marker gene to determine expression thereof, analyzing the expression of the Marker gene according to the methods described herein and determining if the Marker gene is effectively specific for the tumor of origin.

The invention further provides compositions comprising at least one isolated sequence selected from SEQ ID NOs: 11-58.

The invention further provides kits, articles, microarrays or gene chip, diagnostic/prognostic portfolios for conducting the assays described herein and patient reports for reporting the results obtained by the present methods.

The mere presence or absence of particular nucleic acid sequences in a tissue sample has only rarely been found to have diagnostic or prognostic value. Information about the expression of various proteins, peptides or mRNA, on the other hand, is increasingly viewed as important. The mere presence of nucleic acid sequences having the potential to express proteins, peptides, or mRNA (such sequences referred to as “genes”) within the genome by itself is not determinative of whether a protein, peptide, or mRNA is expressed in a given cell. Whether or not a given gene capable of expressing proteins, peptides, or MRNA does so and to what extent such expression occurs, if at all, is determined by a variety of complex factors. Irrespective of difficulties in understanding and assessing these factors, assaying gene expression can provide useful information about the occurrence of important events such as tumorogenesis, metastasis, apoptosis, and other clinically relevant phenomena. Relative indications of the degree to which genes are active or inactive can be found in gene expression profiles. The gene expression profiles of this invention are used to provide a diagnosis and treat patients for CUP.

Sample preparation requires the collection of patient samples. Patient samples used in the inventive method are those that are suspected of containing diseased cells such as cells taken from a nodule in a fine needle aspirate (FNA) of tissue. Bulk tissue preparation obtained from a biopsy or a surgical specimen and laser capture microdissection are also suitable for use. Laser Capture Microdissection (LCM) technology is one way to select the cells to be studied, minimizing variability caused by cell type heterogeneity. Consequently, moderate or small changes in Marker gene expression between normal or benign and cancerous cells can be readily detected. Samples can also comprise circulating epithelial cells extracted from peripheral blood. These can be obtained according to a number of methods but the most preferred method is the magnetic separation technique described in 6136182. Once the sample containing the cells of interest has been obtained, a gene expression profile is obtained using a Biomarker, for genes in the appropriate portfolios.

Preferred methods for establishing gene expression profiles include determining the amount of RNA that is produced by a gene that can code for a protein or peptide. This is accomplished by reverse transcriptase PCR (RT-PCR), competitive RT-PCR, real time RT-PCR, differential display RT-PCR, Northern Blot analysis and other related tests. While it is possible to conduct these techniques using individual PCR reactions, it is best to amplify complementary DNA (cDNA) or complementary RNA (cRNA) produced from MRNA and analyze it via microarray. A number of different array configurations and methods for their production are known to those of skill in the art and are described in for instance, 5445934; 5532128; 5556752; 5242974; 5384261; 5405783; 5412087; 5424186; 5429807; 5436327; 5472672; 5527681; 5529756; 5545531; 5554501; 5561071; 5571639; 5593839; 5599695; 5624711; 5658734; and 5700637.

Microarray technology allows for measuring the steady-state MRNA level of thousands of genes simultaneously providing a powerful tool for identifying effects such as the onset, arrest, or modulation of uncontrolled cell proliferation. Two microarray technologies are currently in wide use, cDNA and oligonucleotide arrays. Although differences exist in the construction of these chins essentially all downstream data analysis and output are the same. The product of these analyses are typically measurements of the intensity of the signal received from a labeled probe used to detect a cDNA sequence from the sample that hybridizes to a nucleic acid sequence at a known location on the microarray. Typically, the intensity of the signal is proportional to the quantity of cDNA, and thus MRNA, expressed in the sample cells. A large number of such techniques are available and useful. Preferred methods for determining gene expression can be found in 6271002; 6218122; 6218114; and 6004755.

Analysis of the expression levels is conducted by comparing such signal intensities. This is best done by generating a ratio matrix of the expression intensities of genes in a test sample versus those in a control sample. For instance, the gene expression intensities from a diseased tissue can be compared with the expression intensities generated from benign or normal tissue of the same type. A ratio of these expression intensities indicates the fold-change in gene expression between the test and control samples.

The selection can be based on statistical tests that produce ranked lists related to the evidence of significance for each gene's differential expression between factors related to the tumor's original site of origin. Examples of such tests include ANOVA and Kruskal-Wallis. The rankings can be used as weightings in a model designed to interpret the summation of such weights, up to a cutoff, as the preponderance of evidence in favor of one class over another. Previous evidence as described in the literature may also be used to adjust the weightings.

In the present invention, 10 markers were chosen that showed significant evidence of differential expression amongst 6 tumor types. The selection process included an ad-hoc collection of statistical tests, mean-variance optimization, and expert knowledge. In an alternative embodiment the feature extraction methods could be automated to select and test markers through supervised learning approaches. As the database grows, the selection of markers can be repeated in order to produce the highest diagnostic accuracy possible at any given state of the database.

A preferred embodiment is to normalize each measurement by identifying a stable control set and scaling this set to zero variance across all samples. This control set is defined as any single endogenous transcript or set of endogenous transcripts affected by systematic error in the assay, and not known to change independently of this error. All markers are adjusted by the sample specific factor that generates zero variance for any descriptive statistic of the control set, such as mean or median, or for a direct measurement. Alternatively, if the premise of variation of controls related only to systematic error is not true, yet the resulting classification error is less when normalization is performed, the control set will still be used as stated. Non-endogenous spike controls could also be helpful, but are not preferred.

Following marker selection, these selected variables are used in a classifier designed to produce as high a classification accuracy as possible. A supervised learning algorithm designed to relate a set of input measurements to an output set of predictors in order to build a model from the 10 inputs to predict the tissue of origin can be used. The problem can be stated as: given training data {(x₁,y), . . . , (x_(n),y)} produce a classifier h:χ→γ which maps a sample x∈χ to its tissue of orign label y∈γ. The predictions are based on the previously resolved cases that are contained in the database and thus compose the training set.

The supervised learning algorithm should find parameters based on the relationships of the input variables to the known outputs that will minimize the expected classification error. These parameters can then be used to predict the tissue of origin from a new sample's input. Examples of these algorithms include linear classification models, quadratic classifiers, tree-based methods, neural networks, and prototype methods such as a k-nearest neighbor classifier or leaming vector quantization algorithms.

One specific embodiment to model the 10 normalized markers is the LDA method, using default parameters, as described in Venables and Ripley (2002). This method is based on Fisher's linear discriminant analysis, where given means {right arrow over (μ)}_(y)=0, {right arrow over (μ)}_(y)=1 and covariances Σ_(y=0),Σ_(y=1) for y class labels 0 and 1, we seek a linear combination of {right arrow over (w)}.{right arrow over (x )} which will have means {right arrow over (w)}.{right arrow over (μ)}_(y=i) and variances ${\overset{->}{w}}^{T}{\sum\limits_{y = i}\quad\overset{->}{w}}$ that will maximize the ratio of the variance between the classes to the variance within the classes: $S = {\frac{\sigma_{between}^{2}}{\sigma_{within}^{2}} = {\frac{\left( {{\overset{->}{w} \cdot {\overset{->}{\mu}}_{y = 1}} - {\overset{->}{w} \cdot {\overset{->}{\mu}}_{y = 0}}} \right)^{2}}{{{\overset{->}{w}}^{T}{\sum\limits_{y = 1}\overset{->}{w}}} + {{\overset{->}{w}}^{T}{\sum\limits_{y = 0}\overset{->}{w}}}} = \frac{\left( {\overset{->}{w} \cdot \left( {{\overset{->}{\mu}}_{y = 1} - {\overset{->}{\mu}}_{y = 0}} \right)} \right)^{2}}{{{\overset{->}{w}}^{T}\left( {\sum\limits_{y = 0}{+ \sum\limits_{y = 1}}} \right)}\overset{->}{w}}}}$

LDA can be generalized to a multiple class discriminant analysis, where y has N possible states, instead of only two. The class means and variances are estimated from the values contained in the database for the choosen markers. In a preferred embodiment, the covariance matrix is weighted by equal prior probabilities of each tumor type subject to the following. Male patients are predicted by a model where the priors are zero for each female reproductive organ tumor group. Likewise, female patients are predicted by a model where the prior is zero for male reproductive organs. In the present invention, the priors are zero for tests on females for prostate and zero for tested males for breast and ovary. Furthermore, samples with a background identical to a class label are tested by a model where the prior probability is zero for that particular class label.

The problem above can be viewed as a maximization of the Rayleigh quotient handled as a generalized eigenvalue problem. The reduced subspace are used in classification by calculating each sample's distance to the centroid in the chosen subspace. The model can be fitted by maximum likelihood, and the posterior probabilities are calculated using Bayes' theorem.

An alternative method may include finding a map of a the n-dimensional feature space, where n is the number of variables used, to a set of classification labels will involve partitioning the feature space into regions, then assigning a classification to each region. The scores of these nearest neighbor type algorithms are related to the distance between decision boundaries and are not necessarily translated into class probabilities.

If there are too many variables to select from, and many of them are random noise, then the variable selection and model risk over-fitting the problem. Therefore, ranked list at various cut-offs are often used as inputs in order to limit the number of variables. Search algorithms such as a genetic algorithm can also be used to select for a sub-set of variables as they test a cost function. Simulated annealing can be attempted to limit the risk of catching the cost function in a local minimum. Nevertheless, these procedures must be validated with samples independent to the selection and modeling process.

Latent variable approaches may also be used. Any unsupervised learning algorithm to estimate low dimensional manifolds from high dimensional space can be used to discover associations between the input variables and how well they can fit a smaller set of latent variables. Although estimations of the effectiveness of the reductions are subjective, a supervised algorithm can be applied on the reduced variable set in order to estimate classification accuracy. Thus a classifier, which can be constructed from the latent variables, can also be built from a set of variables significantly correlated with the latent variables. An example of this would include using variables correlated to the principle components, from a principle component analysis, as inputs to any supervised classification model.

These algorithms can be implemented in any software code that has methods for inputting the variables, training the samples with a function, testing a sample based on the model, and outputting the results to a console. R, Octave, C, C++, Fortran, Java, Perl, and Python all have libraries available under an open source license to perform many of the functions listed above. Commercial packages such as S+ and Matlab are also packaged with many of these methods.

The code performs the following steps in the following order using R version 2.2.1 (http://www.r-project.org) with the MASS (Venables et al. (2002)) library installed. The term LDA refers to the Ida function in the MASS namespace.

-   -   1) CT values for 10 marker genes and 2 controls are stored on a         hard drive for all available training set samples.     -   2) For each sample, subtracting the sample specific average of         the controls from each marker normalizes the 10 marker gene         values.     -   3) The training data set is composed of metastasis with known         sites of origin where each sample has at least one of its target         markers specific for the labeled tissue of origin with a         normalized CT value less than 5.     -   4) LDA constructs 4 sets of 2 LDA models from the training data         in (3). In each set, one model is specific for males, and has         prior odds for breast and ovary set to zero as well as the prior         odds for prostate set to the equivalent priors of the other         class labels. The other model in each pair is specific for         females with the prior odds of prostate set to zero, and with         the priors for breast and ovary set to the equivalent priors         found in the other class labels.         -   a. The first set is used to test CUP samples found in the             colon, the prior odds for colon are set to zero and all             other non-reproductive class labels are set to equivalent             priors.         -   b. A second model set is specific for a CUP found in the             ovary, with prior odds for ovary set to zero and all other             non-reproductive class labels set to equivalent priors.         -   c. A third set is for a CUP found in the lung, with prior             odds for lung set to zero. All other non-reproductive class             labels have equivalent priors.         -   d. The general model used for all other background tissues.             All priors are set equivalently with the exception of the             reproductive specific class labels that are set as defined             in 4.

In order to test a sample, we run an R program that performs the following.

-   -   1) Reads in a test data set.     -   2) Generates a sample specific average of both controls.     -   3) For each sample, uses the sample specific average to subtract         from each marker.     -   4) Replaces any normalized CT generated from a raw CT of 40 with         12.     -   5) For each sample in the test set the following are tested.         -   a. If the average of both controls are greater than 34 than             the sample is labeled as ‘CTR_FAILURE’ with zeros for             posterior probabilities.         -   b. The backgrounds are checked for colon, ovary, or lung. If             a match is found than the gender is checked as well. The             background and gender specific model is then used to             evaluate the sample.         -   c. If breast, pancreas, lungSCC, or prostate is found as the             background label, then a label of             ‘FAILURE_ineligible_sample’ is given to the sample, and the             posterior probabilities are all set to zero.         -   d. The general model for either male or female is used for             all other samples.

The results are formatted and written to a file.

The present invention includes gene expression portfolios obtained by this process.

Gene expression profiles can be displayed in a number of ways. The most common is to arrange raw fluorescence intensities or ratio matrix into a graphical dendogram where columns indicate test samples and rows indicate genes. The data are arranged so genes that have similar expression profiles are proximal to each other. The expression ratio for each gene is visualized as a color. For example, a ratio less than one (down-regulation) appears in the blue portion of the spectrum while a ratio greater than one (up-regulation) appears in the red portion of the spectrum. Commercially available computer software programs are available to display such data including “GeneSpring” (Silicon Genetics, Inc.) and “Discovery” and “Infer” (Partek, Inc.)

Measurements of the abundance of unique RNA species are collected from primary tumors or metastatic tumors from primaries of known origin. These readings along with clinical records including, but not limited to, a patient's age, gender, site of origin of primary tumor, and site of metastasis (if applicable) are used to generate a relational database. The database is used to select RNA transcripts and clinical factors that can be used as marker variables to predict the primary origin of a metastatic tumor.

In the case of measuring protein levels to determine gene expression, any method known in the art is suitable provided it results in adequate specificity and sensitivity. For example, protein levels can be measured by binding to an antibody or antibody fragment specific for the protein and measuring the amount of antibody-bound protein. Antibodies can be labeled by radioactive, fluorescent or other detectable reagents to facilitate detection. Methods of detection include, without limitation, enzyme-linked immunosorbent assay (ELISA) and immunoblot techniques.

Modulated genes used in the methods of the invention are described in the Examples. The genes that are differentially expressed are either up regulated or down regulated in patients with carcinoma of a particular origin relative to those with carcinomas from different origins. Up regulation and down regulation are relative terms meaning that a detectable difference (beyond the contribution of noise in the system used to measure it) is found in the amount of expression of the genes relative to some baseline. In this case, the baseline is determined based on the algorithm. The genes of interest in the diseased cells are then either up regulated or down regulated relative to the baseline level using the same measurement method. Diseased, in this context, refers to an alteration of the state of a body that interrupts or disturbs, or has the potential to disturb, proper performance of bodily functions as occurs with the uncontrolled proliferation of cells. Someone is diagnosed with a disease when some aspect of that person's genotype or phenotype is consistent with the presence of the disease. However, the act of conducting a diagnosis or prognosis may include the determination of disease/status issues such as determining the likelihood of relapse, type of therapy and therapy monitoring. In therapy monitoring, clinical judgments are made regarding the effect of a given course of therapy by comparing the expression of genes over time to determine whether the gene expression profiles have changed or are changing to patterns more consistent with normal tissue.

Genes can be grouped so that information obtained about the set of genes in the group provides a sound basis for making a clinically relevant judgment such as a diagnosis, prognosis, or treatment choice. These sets of genes make up the portfolios of the invention. As with most diagnostic Markers, it is often desirable to use the fewest number of Markers sufficient to make a correct medical judgment. This prevents a delay in treatment pending further analysis as well unproductive use of time and resources.

One method of establishing gene expression portfolios is through the use of optimization algorithms such as the mean variance algorithm widely used in establishing stock portfolios. This method is described in detail in 20030194734. Essentially, the method calls for the establishment of a set of inputs (stocks in financial applications, expression as measured by intensity here) that will optimize the return (e.g., signal that is generated) one receives for using it while minimizing the variability of the return. Many commercial software programs are available to conduct such operations. “Wagner Associates Mean-Variance Optimization Application,” referred to as “Wagner Software” throughout this specification, is preferred. This software uses functions from the “Wagner Associates Mean-Variance Optimization Library” to determine an efficient frontier and optimal portfolios in the Markowitz sense is preferred. Markowitz (1952). Use of this type of software requires that microarray data be transformed so that it can be treated as an input in the way stock return and risk measurements are used when the software is used for its intended financial analysis purposes.

The process of selecting a portfolio can also include the application of heuristic rules. Preferably, such rules are formulated based on biology and an understanding of the technology used to produce clinical results. More preferably, they are applied to output from the optimization method. For example, the mean variance method of portfolio selection can be applied to microarray data for a number of genes differentially expressed in subjects with cancer. Output from the method would be an optimized set of genes that could include some genes that are expressed in peripheral blood as well as in diseased tissue. If sampjes used in the testing method are obtained from peripheral blood and certain genes differentially expressed in instances of cancer could also be differentially expressed in peripheral blood, then a heuristic rule can be applied in which a portfolio is selected from the efficient frontier excluding those that are differentially expressed in peripheral blood. Of course, the rule can be applied prior to the formation of the efficient frontier by, for example, applying the rule during data pre-selection.

Other heuristic rules can be applied that are not necessarily related to the biology in question. For example, one can apply a rule that only a prescribed percentage of the portfolio can be represented by a particular gene or group of genes. Commercially available software such as the Wagner Software readily accommodates these types of heuristics. This can be useful, for example, when factors other than accuracy and precision (e.g., anticipated licensing fees) have an impact on the desirability of including one or more genes

The gene expression profiles of this invention can also be used in conjunction with other non-genetic diagnostic methods useful in cancer diagnosis, prognosis, or treatment monitoring. For example, in some circumstances it is beneficial to combine the diagnostic power of the gene expression based methods described above with data from conventional Markers such as serum protein Markers (e.g., Cancer Antigen 27.29 (“CA 27.29”)). A range of such Markers exists including such analytes as CA 27.29. In one such method, blood is periodically taken from a treated patient and then subjected to an enzyme immunoassay for one of the serum Markers described above. When the concentration of the Marker suggests the return of tumors or failure of therapy, a sample source amenable to gene expression analysis is taken. Where a suspicious mass exists, a fine needle aspirate (FNA) is taken and gene expression profiles of cells taken from the mass are then analyzed as described above. Alternatively, tissue samples may be taken from areas adjacent to the tissue from which a tumor was previously removed. This approach can be particularly useful when other testing produces ambiguous results.

Kits made according to the invention include formatted assays for determining the gene expression profiles. These can include all or some of the materials needed to conduct the assays such as reagents and instructions and a medium through which Biomarkers are assayed.

Articles of this invention include representations of the gene expression profiles useful for treating, diagnosing, prognosticating, and otherwise assessing diseases. These profile representations are reduced to a medium that can be automatically read by a machine such as computer readable media (magnetic, optical, and the like). The articles can also include instructions for assessing the gene expression profiles in such media. For example, the articles may comprise a CD ROM having computer instructions for comparing gene expression profiles of the portfolios of genes described above. The articles may also have gene expression profiles digitally recorded therein so that they may be compared with gene expression data from patient samples. Alternatively, the profiles can be recorded in different representational format. A graphical recordation is one such format. Clustering algorithms such as those incorporated in “DISCOVERY” and “INFER” software from Partek, Inc. mentioned above can best assist in the visualization of such data.

Different types of articles of manufacture according to the invention are media or formatted assays used to reveal gene expression profiles. These can comprise, for example, microarrays in which sequence complements or probes are affixed to a matrix to which the sequences indicative of the genes of interest combine creating a readable determinant of their presence. Alternatively, articles according to the invention can be fashioned into reagent kits for conducting hybridization, amplification, and signal generation indicative of the level of expression of the genes of interest for detecting cancer.

The following examples are provided to illustrate but not limit the claimed invention. All references cited herein are hereby incorporated herein by reference.

EXAMPLE 1

Materials and Methods

Pancreatic Cancer Markers Gene Discovery

RNA was isolated from pancreatic tumor, normal pancreatic, lung, colon, breast and ovarian tissues using Trizol. The RNA was then used to generate amplified, labeled RNA (Lipshutz et al. (1999)) which was then hybridized onto Affymetrix U133A arrays. The data were then analyzed in two ways.

In the first method, this dataset was filtered to retain only those genes with at least two present calls across the entire dataset. This filtering left 14,547 genes. 2,736 genes were determined to be overexpressed in pancreatic cancer versus normal pancreas with a p value of less than 0.05. Forty five genes of the 2,736 were also overexpressed by at least two-fold compared to the maximum intensity found from lung and colon tissues. Finally, six probe sets were found which were overexpressed by at least two-fold compared to the maximum intensity found from lung, colon, breast, and ovarian tissues.

In the second method, this dataset was filtered to retain only those genes with no more than two present calls in breast, colon, lung, and ovarian tissues. This filtering left 4,654 genes. 160 genes of the 4,654 genes were found to have at least two present calls in the pancreatic tissues (normal and cancer). Finally, eight probe sets were selected which showed the greatest differential expression between pancreatic cancer and normal tissues.

Tissue Samples

A total of 260 FFPE metastasis and primary tissues were acquired from a variety of commercial vendors. The samples tested included: 30 breast metastasis, 30 colorectal metastasis, 56 lung metastasis, 49 ovarian metastasis 43 pancreas metastasis, 18 prostate primary and 2 prostate metastases and 32 other origins (6 stomach, 6 kidney, 3 larynx, 2 liver, 1 esophagus, 1 pharynx, 1 bile duct, 1 pleura, 3 bladder, 5 melanoma, 3 lymphoma).

RNA Extraction

RNA isolation from paraffin tissue sections was based on the methods and reagents described in the High Pure RNA Paraffin Kit manual (Roche) with the following modifications. Paraffin embedded tissue samples were sectioned according to size of the embedded metastasis (2-5 mm=9×10 μm, 6-8 mm=6×10 μm, 8-≧10 mm=3×10 μm), and placed in RNase/DNase 1.5 ml Eppendorf tubes. Sections were deparaffinized by incubation in 1 ml of xylene for 2-5 min at room temperature following a 10-20 second vortex. Tubes were then centrifuged and supernatant was removed and the deparaffinization step was repeated. After supernatant was removed, 1 ml of ethanol was added and sample was vortexed for 1 minute, centrifuged and supernatant removed. This process was repeated one additional time. Residual ethanol was removed and the pellet was dried in a 55° C. oven for 5-10 minutes and resuspended in 100 μl of tissue lysis buffer, 16 μl 10% SDS and 80 μl Proteinase K. Samples were vortexed and incubated in a thermomixer set at 400 rpm for 2 hours at 55° C. 325 μl binding buffer and 325 μl ethanol was added to each sample that was then mixed, centrifuged and the supernatant was added onto the filter column. Filter column along with collection tube were centrifuged for 1 minute at 8000 rpm and flow through was discarded. A series of sequential washes were performed (500 μl Wash Buffer I→500 μl Wash Buffer II→300 μl Wash Buffer II) in which each solution was added to the column, centrifuged and flow through discarded. Column was then centrifuged at maximum speed for 2 minutes, placed in a fresh 1.5 ml tube and 90 μl of elution buffer was added. RNA was obtained after a 1 minute incubation at room temperature followed by a 1 minute centrifugation at 8000 rpm. Sample was DNase treated with the addition of 10 μl DNase incubation buffer, 2 μl of DNase I and incubated for 30 minutes at 37° C. DNase was inactivated following the addition of 20 μl of tissue lysis buffer, 18 μl 10% SDS and 40 μl Proteinase K. Again, 325 μl binding buffer and 325 μl ethanol was added to each sample that was then mixed, centrifuged and supernatant was added onto the filter column. Sequential washes and elution of RNA proceeded as stated above with the exception of 50 μl of elution buffer being used to elute the RNA. To eliminate glass fiber contamination carried over from the column RNA was centrifuged for 2 minutes at full speed and supernatant was removed into a fresh 1.5 ml Eppendorf tube. Samples were quantified by OD 260/280 readings obtained by a spectrophotometer and samples were diluted to 50 ng/μl. The isolated RNA was stored in Rnase-free water at −80° C. until use.

TaqMan Primer and Probe Design

Appropriate mRNA reference sequence accession numbers in conjunction with Oligo 6.0 were used to develop TaqMan® CUP assays (lung Markers: human surfactant, pulmonary-associated protein B (HUMPSPBA), thyroid transcription factor 1 (TTF1), desmoglein 3 (DSG3), colorectal Marker: cadherin 17 (CDH17), breast Markers: mammaglobin (MG), prostate-derived ets transcription factor (PDEF), ovarian Marker: wilms tumor 1 (WT1), pancreas Markers: prostate stem cell antigen (PSCA), coagulation factor V (F5), prostate Marker kallikrein 3 (KLK3)) and housekeeping assays beta actin (β-Actin), hydroxymethylbilane synthase (PBGD). Primers and hydrolysis probes for each assay are listed in Table 2. Genornic DNA amplification was excluded by designing assays around exon-intron splicing sites. Hydrolysis probes were labeled at the 5′ nucleotide with FAM as the reporter dye and at 3′ nucleotide with BHQ1-TT as the internal quenching dye.

Quantitative Real-Time Polymerase Chain Reaction

Quantitation of gene-specific RNA was carried out in a 384 well plate on the ABI Prism 7900HT sequence detection system (Applied Biosystems). For each thermo-cycler run calibrators and standard curves were amplified. Calibrators for each Marker consisted of target gene in vitro transcripts that were diluted in carrier RNA from rat kidney at 1×10⁵ copies. Standard curves for housekeeping Markers consisted of target gene in vitro transcripts that were serially diluted in carrier RNA from rat kidney at 1×10⁷, 1×10⁵ and 1×10³ copies. No target controls were also included in each assay run to ensure a lack of environmental contamination. All samples and controls were run in duplicate. qRTPCR was performed with general laboratory use reagents in a 10 μl reaction containing: RT-PCR Buffer (50 nM Bicine/KOH pH 8.2, 11 nM KAc, 8% glycerol, 2.5 mM MgCl₂, 3.5 mM MnSO₄, 0.5 mM each of dCTP, dATP, dGTP and dTTP), Additives (2 mM Tris-Cl pH 8, 0.2 mM Albumin Bovine, 150 mM Trehalose, 0.002% Tween 20), Enzyme Mix (2 U Tth (Roche), 0.4 mg/μl Ab TP6-25), Primer and Probe Mix (0.2 μM Probe, 0.5 μM Primers). The following cycling parameters were followed: 1 cycle at 95° C. for 1 minute; 1 cycle at 55° C. for 2 minutes; Ramp 5%; 1 cycle at 70° C. for 2 minutes; and 40 cycles of 95° C. for 15 seconds, 58° C. for 30 seconds. After the PCR reaction was completed, baseline and threshold values were set in the ABI 7900HT Prism software and calculated Ct values were exported to Microsoft Excel.

One-Step vs. Two-Step Reaction

First strand synthesis was carried out using either 100 ng of random hexamers or gene specific primers per reaction. In the first step, 11.5 μl of Mix-1 (primers and 1 ug of total RNA) was heated to 65° C. for 5 minutes and then chilled on ice. 8.5 μl of Mix-2 (1×Buffer, 0.01 mM DTT, 0.5 mM each dNTP's, 0.25 U/μl RNasin®, 10 U/μl Superscript III) was added to Mix-1 and incubated at 50° C. for 60 minutes followed by 95° C. for 5 minutes. The cDNA was stored at −20° C. until ready for use. qRTPCR for the second step of the two-step reaction was performed as stated above with the following cycling parameters: 1 cycle at 95° C. for 1 minute; 40 cycles of 95° C. for 15 seconds, 58° C. for 30 seconds. qRTPCR for the one-step reaction was performed exactly as stated in the preceding paragraph. Both the one-step and two-step reactions were performed on 100 ng of template (RNA/cDNA). After the PCR reaction was completed, baseline and threshold values were set in the ABI 7900HT Prism software and calculated Ct values were exported to Microsoft Excel.

Generation of a Heatmap

For each sample, a ΔCt was calculated by taking the mean Ct of each CUP Marker and subtracting the mean Ct of an average of the housekeeping Markers (ΔCt=Ct(CUP Marker)−Ct(Ave. HK Marker)). The minimal ΔCt for each tissue of origin Marker set (lung, breast, prostate, colon, ovarian and pancreas) was determined for each sample. The tissue of origin with the overall minimal ΔCt was scored one and all other tissue of origins scored zero. Data were sorted according to pathological diagnosis. Partek Pro was populated with the modified feasibility data and an intensity plot was generated.

Results

Discovery of Novel Pancreatic Tumor of Origin and Cancer Status Markers

First, five pancreas Marker candidates were analyzed: prostate stem cell antigen (PSCA), serine proteinase inhibitor, clade A member 1 (SERPINA1), cytokeratin 7 (KRT7), matrix metalloprotease 11 (MMP11), and mucin4 (MUC4) (Varadhachary et al (2004); Fukushima et al. (2004); Argani et al. (2001); Jones et al. (2004); Prasad et al. (2005); and Moniaux et al. (2004)) using DNA microarrays and a panel of 13 pancreatic ductal adenocarcinomas, five normal pancreas tissues, and 98 samples from breast, colorectal, lung, and ovarian tumors. Only PSCA demonstrated moderate sensitivity (six out of thirteen or 46% of pancreatic tumors were detected) at a high specificity (91 out of 98 or 93% were correctly identified as not being of pancreatic origin) (FIG. 4A). In contrast, KRT7, SERPINA1, MMP11, and MUC4 demonstrated sensitivities of 38%, 31%, 85%, and 31%, respectively, at specificities of 66%, 91%, 82%, and 81%, respectively. These data were in good agreement with qRTPCR performed on 27 metastases of pancreatic origin and 39 metastases of non-pancreatic origin for all Markers except for MMP11 which showed poorer sensitivity and specificity with qRTPCR and the metastases. In conclusion, the microarray data on snap frozen, primary tissue serves as a good indicator of the ability of the Marker to identify a FFPE metastasis as being pancreatic in origin using qRTPCR but that additional Markers may be useful for optimal performance.

Because pancreatic ductal adenocarcinoma develops from ductal epithelial cells that comprise only a small percentage of all pancreatic cells (with acinar cells and islet cells comprising the majority) and because pancreatic adenocarcinoma tissues contain a significant amount of adjacent normal tissue (Prasad et al. (2005); and Ishikawa et al. (2005)), it has been difficult to identify pancreatic cancer Markers (i.e., upregulated in cancer) which would also differentiate this organ from the organs. For use in a CUP panel such differentiation is necessary. The first query method (see Materials and Methods) returned six probe sets: coagulation factor V (F5), a hypothetical protein FLJ22041 similar to FK506 binding proteins (FKBP10), β 6 integrin (ITGB6), transglutaminase 2 (TGM2), heterogeneous nuclear ribonucleoprotein A0 (HNRP0), and BAX delta (BAX). The second query method (see Materials and Methods) returned eight probe sets: F5, TGM2, paired-like homeodomain transcription factor 1 (PITX1), trio isoform mRNA (TRIO), mRNA for p73H (p73), an unknown protein for MGC:10264 (SCD), and two probe sets for claudin18. F5 and TGM2 were present in both query results and, of the two, F5 looked the most promising (FIG. 4B).

Optimization of Sample Prep and qRTPCR Using FFPE Tissues

Next the RNA isolation and qRTPCR methods were optimized using fixed tissues before examining Marker panel performance. First the effect of reducing the proteinase K incubation time from sixteen hours to 3 hours was analyzed. There was no effect on yield. However, some samples showed longer fragments of RNA when the shorter proteinase K step was used (FIG. 5). For example, when RNA was isolated from a one year old block (C22), there was no observed difference in the electropherograms. However, when RNA was isolated from a five year old block (C23), a larger fraction of higher molecular weight RNAs was observed, as assessed by the hump in the shoulder, when the shorter proteinase K digest was used. This trend generally held when other samples were processed, regardless of the organ of origin for the FFPE metastasis. In conclusion, shortening the proteinase K digestion time does not sacrifice RNA yields and may aid in isolating longer, less degraded RNA.

Next, three different methods of reverse transcription were compared: reverse transcription with random hexamers followed by qPCR (two step), reverse transcription with a gene-specific primer followed by qPCR (two step), and a one-step qRTPCR using gene-specific primers. RNA was isolated from eleven metastases and compared Ct values across the three methods for β-actin, human surfactant protein B (HUMSPB), and thyroid transcription factor (TTF) (FIG. 6). There were statistically significant differences (p<0.001) for all comparisons. For all three genes, the reverse transcription with random hexamers followed by qPCR (two step reaction) gave the highest Ct values while the reverse transcription with a gene-specific primer followed by qPCR (two step reaction) gave slightly (but statistically significant) lower Ct values than the corresponding 1 step reaction. However, the 2 step RTPCR with gene-specific primers had a longer reverse transcription step. When HUMSPB and TTF Ct values were normalized to the corresponding β-actin value for each sample, there were no differences in the normalized Ct values across the three methods. In conclusion, optimization of the RTPCR reaction conditions can generate lower Ct values, which may help in analyzing older paraffin blocks (Cronin et al (2004)), and a one step RTPCR reaction with gene-specific primers can generate Ct values comparable to those generated in the corresponding two step reaction.

Diagnostic Performance of a CUP qRTPCR Assay

Next 12 qRTPCR reactions (10 Markers and two housekeeping genes) were performed on 239 FFPE metastases. The Markers used for the assay are shown in Table 2. The lung Markers were human surfactant pulmonary-associated protein B (HUMPSPB), thyroid transcription factor 1 (TTF1), and desmoglein 3 (DSG3). The colorectal Marker was cadherin 17 (CDH17). The breast Markers were mammaglobin (MG) and prostate-derived Ets transcription factor (PDEF). The ovarian Marker was Wilms tumor 1 (WT1). The pancreas Markers were prostate stem cell antigen (PSCA) and coagulation factor V (F5), and the prostate Marker was kallikrein 3 (KLK3). For gene descriptions, see Table 31. TABLE 2 Primer and probe sequences, accession numbers, and amplicon lengths. SEQ SEQ ID ID Target NO Sequence (5′-3′) Description NO SP-B 59 cacagccccgacctttgatga Forward primer 11 ggtcccagagcccgtctca Reverse primer 12 agctgtccagctgcaaaggaa Probe* 13 aagcc cacagccccgacctttgatga Amplicon 14 gaactcagctgtccagctgca aaggaaaagccaagtgagacg ggctctgggacc TTF1 60 ccaacccagacccgcgc Forward primer 15 cgcccatgccgctcatgttca Reverse primer 16 cccgccatctcccgcttcatg Probe* 17 ccaacccagacccgcgcttcc Amplicon 18 ccgccatctcccgcttcatgg gcccggcgagcggcatgaaca tgagcggcatgggcg DSG3 61 gcagagaaggagaagataact Forward primer 19 caa actccagagattcggtaggtg Reverse primer 20 a attgccaagattacttcagat Probe* 21 tacca gcagagaaggagaagataact Amplicon 22 caaaaagaaacccaattgcca agattacttcagattaccaag caacccagaaaatcacctacc gaatctctggagt CDH17 62 tccctcggcagtggaagctta Forward primer 23 tcctcaaactctgtgtgcctg Reverse primer 24 gta ccaaaatcaatggtactcatg Probe* 25 cccgactg tccctcggcagtggaagctta Amplicon 26 caaaacgactgggaagtttcc aaaatcaatggtactcatgcc cgactgtctaccaggcacaca gagtttgagga MG 63 agttgctgatggtcctcatgc Forward primer 27 cacttgtggattgattgtctt Reverse primer 28 gga ccctctcccagcactgctacg Probe* 28 ca agttgctgatggtcctcatgc Amplicon 30 tggcggccctctcccagcact gctacgcaggctctggctgcc ccttattggagaatgtgattt ccaagacaatcaatccacaag tg PDEF 64 cgcccacctggacatctgga Forward primer 31 cactggtcgaggcacagtag Reverse primer 32 tga gtcagcggcctggatgaaag Probe* 33 agcgg cgcccacctggacatctgga Amplicon 34 agtcagcggcctggatgaaa gagcggacttcacctggggc gattcactactgtgcctcga ccagtg WT1 65 gcggagcccaatacagaata Forward primer 35 cac cggggctactccaggcaca Reverse primer 36 tcagaggcattcaggatgtg Probe* 37 cgacg gcggagcccaatacagaata Amplicon 38 cacacgcacggtgtcttcag aggcattcaggatgtgcgac gtgtgcctggagtagccccg PSCA 66 ctgttgatggcaggcttggc Forward primer 39 ttgctcacctgggctttgca Reverse primer 40 gcagccaggcactgccctgc Probe* 41 t ctgttgatggcaggcttggc Amplicon 42 cctgcagccaggcactgccc tgctgtgctactcctgcaaa gcccaggtgagcaa F5 67 tgaagaaatatcctgggatt Forward primer 43 attca tatgtggtatcttctggaat Reverse primer 44 atcatca acaaagggaaacagatattg Probe* 45 aagactc tgaagaaatatcctgggatt Amplicon 46 attcagaatttgtacaaagg gaaacagatattgaagactc tgatgatattccagaagata ccacata KLK3 68 cccccagtgggtcctcaca Forward primer 47 aggatgaaacaagctgtgcc Reverse primer 48 ga caggaacaaaagcgtgatct Probe* 49 tgctgg cccccagtgggtcctcacag Amplicon 50 ctgcccactgcatcaggaac aaaagcataatcttgctggg tcggcacagcttgtttcatc ct β actin 69 gccctgaggcactcttcca Forward primer 51 cggatgtccacgtcacactt Reverse primer 52 ca cttccttcctgggcatggag Probe* 53 tcctg gccctgaggcactcttccag Amplicon 54 ccttccttcctgggcatgga gtcctgtggcatccacgaaa ctaccttcaactccatcatg aagtgtgacgtggacatccg PBGD 70 ccacacacagcctactttcc Forward primer 55 aa tacccacgcgaatcactctc Reverse primer 56 a aacggcaatgcggctgcaac Probe* 57 ggcggaa ccacacacagcctactttcc Amplicon 58 aagcggagccatgtctggta acggcaatgcggctgcaacg gcggaagaaaacagcccaaa gatgagagtgattcgcgtgg gta *Probes are 5′FAM-3′BHQ1-TT

Analysis of the normalized Ct values in a heat map revealed the high specificity of the breast and prostate Markers, moderate specificity of the colon, lung, and ovarian, and somewhat lower specificity of the pancreas Markers. Combining the normalized qRTPCR data with computational refinement improves the performance of the Marker panel. Results were obtained from the combined normalized qRTPCR data with the algorithm and the accuracy of the qRTPCR assay was determined.

Discussion

In this example, microarray-based expression profiling was used on primary tumors to identify candidate Markers for use with metastases. The fact that primary tumors can be used to discover tumor of origin Markers for metastases is consistent with several recent findings. For example, Weigelt and colleagues have shown that gene expression profiles of primary breast tumors are maintained in distant metastases. Weigelt et al. (2003). Italiano and coworkers found that EGFR status, as assessed by IHC, was similar in 80 primary colorectal tumors and the 80 related metastases. Italiano et al. (2005). Only five of the 80 showed discordance in EGFR status. Italiano et al. (2005). Backus and colleagues identified putative Markers for detecting breast cancer metastasis using a genome-wide gene expression analysis of breast and other tissues and demonstrated that mammaglobin and CK19 detected clinically actionable metastasis in breast sentinel lymph nodes with 90% sensitivity and 94% specificity. Backus et al. (2005).

The microarray-based studies with primary tissue confirmed the specificity and sensitivity of known Markers. As a result, with the exception of F5, all of the Markers used have high specificity for the tissues studied here. Argani et al (2001; Backus et al. (2005); Cunha et al. (2005); Borgono et al. (2004); McCarthy et al. (2003); Hwang et al. (2004); Fleming et al. (2000); Nakamura et al. (2002); and Khoor et al. (1997). A recent study determined that, using IHC, PSCA is overexpressed in prostate cancer metastases. Lam et al. (2005). Dennis et al. (2002) also demonstrated that PSCA could be used as a tumor of origin Marker for pancreas and prostate. As shown herein, strong expression of PSCA is found in some prostate tissues at the RNA level but, because by including PSA in the assay, one can now segregate prostate and pancreatic cancers. A novel finding of this study was the use of F5 as a complementary (to PSCA) Marker for pancreatic tissue of origin. In both the microarray data set with primary tissue and the qRTPCR data set with FFPE metastases, F5 was found to complement PSCA (FIG. 4 and Table 3) TABLE 3 feasibility data Breast Colon Lung Other Ovary Pancreas Prostate Total Total tested 30 30 56 32 49 43 20 260 #Correct 22 27 45 16 43 31 20 204 #Other/No test 1 1 3 n/a 1 4 0 10 #Incorrect 7 2 8 16 5 8 0 46 % Tested 96.67 96.67 94.64 100 97.96 90.70 100 96.15 % Correct of tested 75.86 93.10 84.91 0 89.58 79.49 100 81.60 Correct of total (%) 73.33 90.00 80.36 50.00 87.76 72.09 100 78.46 Previous investigators have generated CUP assays using IHC or microarrays. Su et al. (2001); Ramaswamy et al. (2001); and Bloom et al. (2004). More recently, SAGE has been coupled to a small qRTPCR Marker panel. Dennis (2002); and Buckhaults et al. (2003). This study is the first to combine microarray-based expression profiling with a small panel of qRTPCR assays. Microarray studies with primary tissue identified some, but not all, of the same tissue of origin Markers as those identified previously by SAGE studies. Some studies have demonstrated that a modest agreement between SAGE- and DNA microarray-based profiling data exists and that the correlation improves for genes with higher expression levels. van Ruissen et al. (2005); and Kim (2003). For example, Dennis and colleagues identified PSA, MG, PSCA, and HUMSPB while Buckhaults and coworkers (Dennis et al. (2002)) identified PDEF. Executing the CUP assay using qRTPCR is preferred because it is a robust technology and may have performance advantages over IHC. Al-Mulla et al. (2005); and Haas et al. (2005). As shown herein, the qRTPCR protocol was improved through the use of gene-specific primers in a one-step reaction. This is the first demonstration of the use of gene-specific primers in a one-step qRTPCR reaction with FFPE tissue. Other investigators have either done a two step qRTPCR (cDNA synthesis in one reaction followed by qPCR) or have used random hexamers or truncated gene-specific primers. Abrahamsen et al. (2003); Specht et al. (2001); Godfrey et al. (2000); Cronin et al. (2004); and Mikhitarian et al. (2004).

EXAMPLE 2

CUP FFPE Total RNA Isolation Protocol

(Highpure kit Cat#3270289)

Purpose:

Isolation of total RNA from FFPE tissue

Procedure:

Preparation of Working Solutions

1. Proteinase K (PK) in Kit

Dissolve lyophilizate in 4.5 ml Elution Buffer. Aliquot and store at −20° C., stable for 12 months.

PK-4×250 mg (cat #3115852)

Dissolve lyophilizate in 12.5 ml of Elution Buffer (1×TE Buffer (pH 7.4-7). Aliquot and store at −20° C.

2. Wash Buffer I

Add 60 ml absolute ethanol to Wash Buffer I, store at RT.

3. Wash Buffer II

Add 200 ml absolute ethanol to Wash Buffer II, store at RT.

4. DNase I

Dissolve lyophilizate in 400 μl Elution Buffer. Aliquot and store at −20° C., stable for 12 months.

Sectioning Paraffm Blocks ˜30-45 Minutes for 12 Blocks (12 Blocks×2 Tubes=24 Tubes)

Sections cut from the block should be processed immediately for RNA extraction

-   1. Use a clean sharp razor blade on Microtome to cut 6×10 micron     thick sections from trimmed tissue blocks (size 3-4×5-10 mm).     -   Note: New block-Discard wax sections until obtained tissue         section. Used block-Discard first 3 tissue sections -   2. Immediately place cut tissue in 1.5 mL microfuge tubes and     tightly cap to minimize moisture.

3. It is recommended to take the number of sections based on size of tumor shown in Table 4. TABLE 4 Size of MET Sections/Tube 8-10 mm  6 6-8 mm 12 2-4 mm 18 Deparaffinization ˜30-45 Minutes

-   1. Add 1.0 ml xylene to each sample and vortex vigorously for 10-20     sec and incubate RT 2-5 min. Centrifuge at full speed 2 min. Remove     the supernatant carefully.     -   Note: if the tissue appears to be floating, centrifuge for an         additional 2 min. -   2. Repeat step 1. -   3. Centrifuge at full speed 2 min. Remove the supernatant. -   4. Add 1 ml ethanol abs. and vortex vigorously 1 min. Centrifuge at     full speed 2 min. Remove the supernatant. -   5. Repeat step 4. -   6. Blot the tube briefly onto a paper towel to get rid of ethanol     residues. -   7. Dry the tissue pellet for 5-10 min at 55° C. in oven.     Note: it is critical that the ethanol is completely removed and the     pellets are thoroughly dry, residual ethanol can inhibit PK     digestion.     Note: if PK is in −20C, warm in RT 20-30 min.     RNA Extraction ˜2.5-3 Hours -   1. Add 100 μl Tissue Lysis Buffer, 16 μl 10% SDS and 80 μl     Proteinase K working solution to one tissue pellet, vortex briefly     in several intervals and incubate 2 hrs at 55° C. shaking 400 rpm. -   2. Add 325 μl Binding Buffer and 325 μl ethanol abs. Mix gently by     pipetting up and down. -   3. Centrifuge the lysate at full speed for 2 min. -   4. Combine the filter tube and the collection tube (12 tubes), and     pipet the lysate supematant into the filter. -   5. Centrifuge for 30 sec at 8000 rpm and discard the flowthrough.     Note: Step 4-5 can be repeated, if RNA needs to be pooled with 2     more tissue pellet preparations. -   6. Repeat the centrifugation at 8000 rpm for 30 sec to dry the     filter. -   7. Add 500 μl Wash Buffer I working solution to the column and     centrifuge for 15-30 sec at 8000 rpm, discard the flowthrough. -   8. Add 500 μl Wash Buffer II working solution. Centrifuge for 15-30     sec at 8000 rpm, discard the flowthrough. -   9. Add 300 μl Wash Buffer II working solution, centrifuge for 15-30     sec at 8000 rpm, discard the flowthrough. -   10. Centrifuge the High Pure filter for 2 min at maximum speed. -   11. Place the High Pure filter tube into a fresh 1.5 ml tube and add     90 μl Elution Buffer. Incubate for 1-2 min at room temperature.     Centrifuge for 1 min at 8000 rpm.     DNase I Treatment ˜1.5 Hours -   12. Add 10 μl of 10×DNase Incubation Buffer and 1.0 μl DNase I     working solution to the eluate and mix. Incubate for 45 min at     37° C. (or 2.0 μl DNase I for 30 min). -   13. Add 20 μl Tissue Lysis Buffer, 18 μl 10% SDS and 40 μl     Proteinase K working solution. Vortex briefly. Incubate for 30 min     (30-60 min.) at 55° C. -   14. Add 325 μl Binding Buffer and 325 μl ethanol abs. Mix and pipet     into a fresh High Pure filter tube with collection tube (12 tubes). -   15. Centrifuge for 30 sec at 8000 rpm and discard the flowthrough. -   16. Repeat the centrifugation at 8000 rpm for 30 sec to dry the     filter. -   17. Add 500 μl Wash Buffer I working solution to the column.     Centrifuge for 15 sec at 8000 rpm, discard the flowthrough. -   18. Add 500 μl Wash Buffer II working solution. Centrifuge for 15     sec at 8000 rpm, discard the flowthrough. -   19. Add 300 μl Wash Buffer II working solution. Centrifuge for 15     sec at 8000 rpm, discard the flowthrough. -   20. Centrifuge the High Pure filter for 2 min at maximum speed. -   21. Place the High Pure filter tube into a fresh 1.5 ml tube. Add 50     μl Elution Buffer; incubate for 1-2 min at room temperature.     Centrifuge for 1 min at 8000 rpm to collect the eluated RNA. -   22. Centrifuge the eluate for 2 min at full speed and transfer     supernatant to a new tube without disturbing glass fibers at the     bottom. -   23. Take 260/280 OD reading and dilute to 50 ng/μl. Store at −80° C.

CUP ASR Assay Protocol (ABI 7900)

Purpose: Use qRTPCR to determine tissue of origin of a CUP sample

Control Setup:

1. Positive Controls (Refer to Table 5 and Plate C in Plate Setup, FIG. 7) TABLE 5 Serial dilutions of IVT - 5 μl 1 × 10⁸ into 470 μl water and 25 μl of 10000 rRNA = 1E6. Table 5. Dilute 50,000 CE/μl rRNA to 500 CE/μl - 5 μl 50,000 CE/μl + 495 μl H₂O Aliqouts 10 μl per strip tube (2 plates); Place Mix at −80° C. until ready for use. IVT Control CE/μl Sample Water Bkgd rRNA BACTIN 100E+05 50 425 25 CDH17 100E+05 50 425 25 DSG3 100E+05 50 425 25 F5 100E+05 50 425 25 Hump 100E+05 50 425 25 MG 100E+05 50 425 25 PBGD 100E+05 50 425 25 PDEF 100E+05 50 425 25 PSCA 100E+05 50 425 25 TTF1 100E+05 50 425 25 WT1 100E+05 50 425 25

2. Standard Curves (Refer to Table 6 and Plate C in Plate Setup, FIG. 7)

Step 1: Standard curve was setup exactly as shown in Table 6. TABLE 7 Stock Solution - 1 × 10⁸ IVT. Dilute 50,000 CE/μl rRNA to 500 CE/μl - 5 μl 50,000 CE/μl + 495 μl H₂O IVT Control CE/μl Sample Water Bkgd rRNA BACT1N-1 100E+07 50 425 25 BACTIN-2 100E+06 50 425 25 BACTIN-3 100E+05 50 425 25 BACT1N-4 100E+04 50 425 25 BACTIN-5 100E+03 50 425 25 PBGD-1 100E+07 50 425 25 PBGD-2 100E+06 50 425 25 PBGD-3 100E+05 50 425 25 PBGD-4 100E+04 50 425 25 PBGD-5 100E+03 50 425 25

Aliqouts 10 μl per strip tube (2 plates); Place Mix at −80° C. until ready for use.

Enzyme Mix:

1. Master Mix: Enzyme (Tth)/Antibody (TP6-25), see Table 7. TABLE 7 Reagent 2x Enzyme Tth (5 U/μl) 600.00 Antibody: TP6-25 (1 mg/ml) 600.00 Water 300.00 Total 1500.00

Aliquot 500 μl/tube andfreeze at −20° C.

CUP Master Mix:

1. 2.5×CUP Master Mix (Tables 8-11): TABLE 8 ml 5x Additives 2.5x Conc. 0.50 1M Tris-C1 pH 8 5 mM 1.25 40 mg/ml Albumin, bovine 500 μg/ml 37.50 1M stock Trehalose 375 mM 2.5 20% v Tween 20 0.50% 7.00 ddH₂O 48.75

Allow reagent to fully mix >15 minutes TABLE 9 ml 5x Additives 2.5 x Conc 12.50 1M Bicine/Potassium Hydroxide pH 8.2  125 mM 5.75 5M Potassium Acetate 287.5 mM  20.00 Glycerol (V × D = M -> 19.6 × 1.26 = 24.6 g) 20% 1.25 500 mM Magnesium Chloride 6.25 mM 1.75 500 mM Manganese Chloride 8.75 mM 5.00 ddH₂O 46.25

Allow reagent to fully mix >15 minutes; Combine above mixes into sterile container—add the following TABLE 10 ml 5x Additives 2.5x Conc. 1.25 100 mM dATP 1.25 mM 1.25 100 mM dCTP 1.25 mM 1.25 100 mM dTTP 1.25 mM 1.25 100 mM dGTP 1.25 mM 100.00

Allow reagent to fully mix >15 minutes; Aliquot 1.8 ml/tube andfreeze at −20° C. TABLE 11 Primer/Probe Stock (μM) FC (μM) μl Forward Primer 100 10 100.0 Reverse Primer 100 10 100.0 Probe (5′FAM/3′BHQ1-TT) 100 4 40.0 DI Water 760.0 Total 1000.0 Primer and Probe Mix:

Aliquot 250 μl/tube and freeze at −20° C.

Reaction Mix:

1. CUP Master Mix (CMM): (Refer to Tables 12-14 and Plate A in Plate Setup, FIG. 7) TABLE 12 Reagent FC X1 (10 μl) 450 2.5 x CUP Master Mix 1X 4.00 1800 ROX 1x 0.20 90 2x TthAb Mix 2U 1.00 450 Water 2.3 1035 Total 7.50 3375 Preferably, each run/plate will have no more than 356 reactions: 12 samples with 12 Markers (288 reactions with 2 replicates for each)+10 std curve controls in duplicate (20)+2 positive and 2 negative controls for each Marker. (4×12=48)

Adjust water for sample volume—4.3 μl Sample MAX; Mix Well TABLE 13 Reagent FC X1 (10 μl) 34 Primers 10 μM/Probe 4 μM 0.5 μM/0.2 μM 0.50 17 CMM 1x 7.50 255 Total 8.00 272

2. ToO Markers: Mix Well TABLE 14 Reagent FC X1 (10 μl) 44 Primers 10 μM/Probe 4 μM 0.5 μM/0.2 μM 0.50 22 CMM 1x 7.50 330 Total 8.00 352

3. β-Actin and PBGD Markers: Mix Well

Sample Setup: TABLE 15 Sample Sample ID Conc Water Added = 50 ng/μl A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 1. CUP Samples: 12 samples in 96 well plate: A1-A12 (Refer to Table 16 and Plate B in Plate Setup, FIG. 7); Aliquot 50 μl of 50 ng/μl (2 μl/rxn) Load Plate:

1. 384 Well Plate Setup: (Refer to Plate D in Plate Setup, FIG. 7)

2 μl of sample and 8 μl of CMM are loaded onto the plate. (sample=50 ng/μl)

4 μl of sample and 6 μl of CMM are loaded on to the plate (sample=25 ng/μl)

The plate is sealed and labeled. Centrifuge at 2000 rpm for 1 min.

ABI 7900HT Setup: Place in the ABI 7900. Select the program “CUP 384” and hit start. TABLE 16 Thermocycling conditions 95 C × 60 s 55 C × 2 m RAMP 5% 70 C × 2 m 40 cycles of 95 C × 15 s 58 C × 30 s ROX Turned On

Data are analyzed, Ct's extracted and inserted in Algorithm

EXAMPLE 3

CUP Algorithm

The actin normalized ΔCt values for HPT, MGB, PDEF, PSA, SP-B, TFF, DSG, WT1, PSCA, and F5 are placed into 6 sets based on the tissue of origin from which originally selected. The constants 9.00, 11.00, 7.50, 5.00, 10.00, 9.50, 6.50, 8.00, 9.00, and 8.00 are subtracted from each ΔCt respectively. Then, for each sample the minimum CT value from each of the 6 sets (HPT, min (MGB, or PDEF), PSA, min (SP-B, TFF, or DSG), WT1, and min (PSCA, or F5)) is selected as the representative variable for the group. These variables, and the metastatic site are used to classify the sample using linear discriminants. Two different models, one for males and one for females, should be constructed from the training data using the MASS library function ‘Ida’ (Venables and Ripley) in R (version 2.0.1). A posterior probability for each tissue of origin is then calculated using the ‘predict’ function for either the male or female model.

The variables used in the male models are HPT, PSA, the minimum of (‘SP-B’, ‘TFF’, ‘DSG3’), the minimum of (‘PSCA’, ‘F5’), and the metastatic site. The metastatic site category has 4 levels corresponding to colon, lung, ovary, and all other tissues. For the female models, the variables are HPT, the minimum of (‘MGB’, ‘PDEF’), the minimum of (‘SP-B’, ‘TFF’, ‘DSG3’), WT1, the minimum of (‘PSCA’, ‘F5’), and the metastatic site.

Example R Code: #Training the male model dat.m<-CUP2.MIN.NORM[,c (‘HPT’,‘PSA’,‘SP.B.TTF.DSG3’,‘PSCA.F5’,‘Class’,‘background’)] CUP.lda.m<-lda(Class˜.,dat.m,prior= c(0,0.09,0.23,0.43,0,0.16,0.02)/sum(c (0,0.09,0.23,0.43,0,0.16,0.02))) #Training the female model dat.f<-CUP2.MIN.NORM[,c (‘HPT’,‘MFB.PDEF’,‘SP.B.TTF.DSG3’,‘WT1’, ‘PSCA.F5’,‘Class’,‘background’)] CUP.lda.f<-lda(Class˜.,dat.f,prior= c(0.03,0.09,0.23,0.43,0.04,0.16,0)/sum(c (0.03,0.09,0.23,0.43,0.04,0.16,0))) #if unknown sample (i) is male predict(CUP.lda.m, CUP2.MIN.NORM.TEST[i,]) #if unknown sample (i) is female predict(CUP.lda.f, CUP2.MIN.NORM.TEST[i,]) To run this code, a data frame called CUP2.MIN.NORM needs to contain the training data with the minimum value calculated for each tissue of origin set as described above.

Class corresponds to the tissue of origin, and background corresponds to the metastatic sites described above.

The test data can be contained in CUP2.MIN.NORM.TEST, and a specific sample at row i can be tested using the predict function. Again, the test data must be in the same format as the training set and have the minimum value adjustments applied to it as well.

EXAMPLE 4

CUP Resolved Samples

48 CUP resolved and unresolved samples were compared to determine the correlation to true CUP samples. The methods used were those described in Examples 1-3. The results obtained are presented in Table 17. 11 samples were tested of unresolved CUP, diagnosis was made on 8 samples, 3 were of other category. TABLE 17 No Sample category Sample # Correct Incorrect test Accuracy % Known ToO 15 11 3 1 79 Resolved CUP 22 17 4 1 81 Unresolved CUP 11 8 N/a 3 73

EXAMPLE 5

CUP Assay Limits

FIG. 8 depicts the results obtained, using the methods described in Examples 1-3, to determine the limits of the CUP assays. Assay performance was tested over a range of RNA concentrations and it was found that CUP assays are efficient in the range of from 100-12.5 ng RNA.

EXAMPLE 6

qRTPCR Assay

Materials and Methods. Frozen Tissue Samples for Microarray Analysis. A total of 700 frozen primary human tissues were used for gene expression microarray profiling. Samples were obtained from variety of academic institutions, including Washington University (St. Louis, Mo.), Erasmus Medical Center (Rotterdam, Netherlands), and commercial tissue bank companies, including Genomics Collaborative, Inc (Cambridge, Mass.), Asterand (Detroit, Mich.), Oncomatrix (La Jolla, Calif.) and Clinomics Biosciences (Pittsfield, Mass.). For each specimen, patient demographic, clinical and pathology information was collected as well. The histopathological features of each sample were reviewed to confirm diagnosis, and to estimate sample preservation and tumor content.

RNA extraction and Affymetrix GeneChip Hybridization. Frozen cancer samples with greater than 70% tumor cells, benign and normal samples were dissected and homogenized with mechanical homogenizer (UltraTurrex T8, Germany) in Trizol reagent (Invitrogen, Carlsbad, Calif.). Tissue was homogenized in Trizol reagent by following the standard Trizol protocol for RNA isolation from frozen tissues (Invitrogen, Carlsbad, Calif.). After centrifugation the top liquid phase was collected and total RNA was precipitated with isopropyl alcohol at −20° C. RNA pellets were washed with 75% ethanol, resolved in water and stored at −80° C. until use.

RNA quality was examined with an Agilent 2100 Bioanalyzer RNA 6000 Nano Assay (Agilent Technologies, Palo Alto, Calif.). Labeled cRNA was prepared and hybridized with the high-density oligonucleotide array Hu133A Gene Chip (Affymetrix, Santa Clara, Calif.) containing a total of 22,000 probe sets according to standard manufacturer protocol. Arrays were scanned using Affymetrix protocols and scanners. For subsequent analysis, each probe set was considered a separate gene. Expression values for each gene were calculated using Affymetrix Gene Chip analysis software MAS 5.0. All chips met three quality control standards: the percent “present” call for the array was greater than 35%, the scale factor was less than 12 when scaled to a global target intensity of 600, and the average background level was less than 150.

Marker Candidate Selection. For selection of tissue of origin (ToO) Marker candidates for lung, colon, breast, ovarian, and prostate tissues, expression levels of the probe sets were measured in the RNA samples covering a total of 682 normal, benign, and cancerous tissues from breast, colon, lung, ovarian, prostate. Tissue specific Marker candidates were selected based on number of statistical queries.

In order to generate pancreatic candidates, gene expression profiles of 13 primary pancreas ductal adenocarcinoma, 5 pancreas normal and 98 lung, colon, breast and ovarian cancer specimens was used to select pancreas adenocarcinoma Markers. Two queries were performed. In the first query, data set containing 14547 genes with at least 2 “present” calls in pancreas samples was created. A total of 2736 genes that overexpressed in pancreas cancer compare to normal was identified by T-test (p<0.05) were identified. Genes which minimal expression at 11th percentile of pancreas cancer was at least 2 fold higher that the maximum in colon and lung cancer was selected, making 45 probe sets. As a final step, 6 genes with maximum expression at least 2 fold higher than maximum expression in colon, lung, breast, and ovarian cancers were selected. In a second query, data set of 4654 probe sets with at most 2 “present” calls in all breast, colon, lung and ovarian specimens was created. A total of 160 genes that have at least 2 “present” calls in pancreas normal and cancer samples were selected. Out of 160 genes, 10 genes were selected after comparing their expression level between pancreas and normal tissues. Results of both pancreas queries were combined.

In addition to gene expression profiles analysis, a few Markers were selected from literature. Results of all queries were combined to make a short list of ToO Marker candidates for each tissue type. Sensitivity and specificity of each Marker were estimated. Markers that demonstrated the best ability to differentiate tissues by their origin were nominated for RT-PCR testing based on Markers redundancy and complementarity.

FFPE metastatic carcinoma ofknown origin and CUP tissues. A total of 386 FFPE metastatic carcinomas (Stage III-IV) of known origin and 24 FFPE prostate primary adenocarcinomas were acquired from a variety of commercial vendors, including Proteogenex (Los Angeles, Calif.), Genomics Collaborative, Inc. (Cambridge, Mass.), Asterand (Detroit, Mich.), Ardais (Lexington, Mass.) and Oncomatrix (La Jolla, Calif.). An independent set of 48 metastatic carcinoma of known primary and CUP tissues was obtained from Albany Medical College (Albany, N.Y.). For each specimen, patient demographic, clinical and pathology information was collected as well. The histopathological features of each sample were reviewed to confirm diagnosis, and to estimate sample preservation and tumor content. For metastatic samples, diagnoses of metastatic carcinoma and tissue of origin were unequivocally established based on patient's clinical history and histological evaluation of metastatic carcinoma in comparison to corresponding primaries.

RNA Isolation from FFPE samples. RNA isolation from paraffin tissue sections was as described in the High Pure RNA Paraffin Kit manual (Roche) with the following modifications. Paraffin embedded tissue samples were sectioned according to size of the embedded metastasis (2-5 mm=9×10 μm, 6-8 mm=6×10 μm, 8-≧10 mm=3×10 μm). Sections were deparaffinized as described by Kit manual, the tissue pellet was dried in a 55° C. oven for 5-10 minutes and resuspended in 100 μl of tissue lysis buffer, 16 μl 10% SDS and 80 μl Proteinase K. Samples were vortexed and incubated in a thermomixer set at 400 rpm for 2 hours at 55° C. Subsequent sample processing was performed according High Pure RNA Paraffin Kit manual. Samples were quantified by OD 260/280 readings obtained by a spectrophotometer and samples were diluted to 50 ng/μl. The isolated RNA was stored in RNase-free water at −80° C. until use.

qRTPCRfor Marker candidates pre-screening. One μg total RNA from each sample was reverse-transcribed with random hexamers using Superscript II reverse transcriptase according to the manufacturer's instructions (Invitrogen, Carlsbad, Calif.). Primers and MGB-probes for the tested gene Marker candidates and the control gene ACTB were designed using Primer Express software (Applied Biosystems, Foster City, Calif.) either ABI Assay-on-Demand (Applied Biosystems, Foster City, Calif.) were used. All in-house designed primers and probes were tested for optimal amplification efficiency above 90%. RT-PCR amplification was carried out in a 20 ml reaction mix containing 200 ng template cDNA, 2× TaqMan® universal PCR master mix (10 ml) (Applied Biosystems, Foster City, Calif.), 500 nM forward and reverse primers, and 250 nM probe. Reactions were run on an ABI PRISM 7900HT Sequence Detection System (Applied Biosystems, Foster City, Calif.). The cycling conditions were: 2 min of AmpErase UNG activation at 50° C., 10 min of polymerase activation at 95° C. and 50 cycles at 95° C. for 15 sec and annealing temperature (60° C.) for 60 sec. In each assay, “no-template” control along with template cDNA was included in duplicate for both the gene of interest and the control gene. The relative expression of each target gene was represented as ΔCt, which is equal to Ct of the target gene subtracted by Ct of the control gene (ACTB).

Optimized One-step qRTPCR. Appropriate mRNA reference sequence accession numbers in conjunction with Oligo 6.0 were used to develop TaqMan® CUP assays (lung Markers: human surfactant, pulmonary-associated protein B (HUMPSPBA), thyroid transcription factor 1 (TTF1), desmoglein 3 (DSG3), colorectal Marker: cadherin 17 (CDH17), breast Markers: mammaglobin (MG), prostate-derived ets transcription factor (PDEF), ovarian Marker: wilms tumor 1 (WT1), pancreas Markers: prostate stem cell antigen (PSCA), coagulation factor V (F5), prostate Marker kallikrein 3 (KLK3)) and housekeeping assays beta actin (β-Actin), hydroxymethylbilane synthase (PBGD). Gene specific primers and hydrolysis probes for the optimized one-step qRT-PCR assay are listed in Table 2 (SEQ ID NOs: 11-58). Genomic DNA amplification was excluded by designing the assays around exon-intron splicing sites. Hydrolysis probes were labeled at the 5′ nucleotide with FAM as the reporter dye and at 3′ nucleotide with BHQ1-TT as the internal quenching dye.

Quantitation of gene-specific RNA was carried out in a 384 well plate on the ABI Prism 7900HT sequence detection system (Applied Biosystems). For each thermo-cycler run calibrators and standard curves were amplified. Calibrators for each Marker consisted of target gene in vitro transcripts that were diluted in carrier RNA from rat kidney at 1×10⁵ copies. Standard curves for housekeeping Markers consisted of target gene in vitro transcripts that were serially diluted in carrier RNA from rat kidney at 1×10⁷, 1×10⁵ and 1×10³ copies. No target controls were also included in each assay run to ensure a lack of environmental contamination. All samples and controls were run in duplicate. qRTPCR was performed with general laboratory use reagents in a 10 μl reaction containing: RT-PCR Buffer (50 nM Bicine/KOH pH 8.2, 115 nM KAc, 8% glycerol, 2.5 mM MgCl₂, 3.5 mM MnSO₄, 0.5 mM each of dCTP, DATP, dGTP and dTTP), Additives (2 mM Tris-Cl pH 8, 0.2 mM Albumin Bovine, 150 mM Trehalose, 0.002% Tween 20), Enzyme Mix (2 U Tth (Roche), 0.4 mg/μl Ab TP6-25), Primer and Probe Mix (0.2 μM Probe, 0.5 μM Primers). The following cycling parameters were followed: 1 cycle at 95° C. for 1 minute; 1 cycle at 55° C. for 2 minutes; Ramp 5%; 1 cycle at 70° C. for 2 minutes; and 40 cycles of 95° C. for 15 seconds, 58° C. for 30 seconds. After the PCR reaction was completed, baseline and threshold values were set in the ABI 7900HT Prism software and calculated Ct values were exported to Microsoft Excel.

One-Step vs. Two-Step Reaction. For comparison of two-step with one-step RT-PCR reactions, first strand synthesis of two-step reaction was carried out using either 100 ng of random hexamers or gene specific primers per reaction. In the first step, 11.5 μl of Mix-1 (primers and 1 μg of total RNA) was heated to 65° C. for 5 minutes and then chilled on ice. 8.5 μl of Mix-2 (1×Buffer, 0.01 mM DTT, 0.5 mM each dNTP's, 0.25 U/μl RNasin®, 10 U/μl Superscript III) was added to Mix-1 and incubated at 50° C. for 60 minutes followed by 95° C. for 5 minutes. The cDNA was stored at −20° C. until ready for use. qRTPCR for the second step of the two-step reaction was performed as stated above with the following cycling parameters: 1 cycle at 95° C. for 1 minute; 40 cycles of 95° C. for 15 seconds, 58° C. for 30 seconds. qRTPCR for the one-step reaction was performed exactly as stated in the preceding paragraph. Both the one-step and two-step reactions were performed on 100 ng of template (RNA/cDNA). After the PCR reaction was completed baseline and threshold values were set in the ABI 7900HT Prism software and calculated Ct values were exported to Microsoft Excel.

Algorithm development. Linear discriminators were constructed using the MASS (Venables and Ripley) library function ‘Ida’ in the R language (version 2.1.1). The model used is dependent on the tissue from which the metastasis was extracted from, as well as the gender of the patient. When a lung, colon, or ovarian site of metastasis is encountered, the class prior is set to zero for the class that is equivalent to the site of metastasis. Furthermore, the prior odds are set to zero for the breast and ovary class in male patients, whilst in female patients, the prostate class' prior is set to zero. All other prior odds used in the models are equivalent. Furthermore classification for each sample is based on the highest posterior probability determined by the model for each class. To estimate the models performance, leave-one-out cross-validation was performed. In addition to this, the data sets were randomly split in halves, while preserving the proportional relationship between each class, into training and testing sets. This random splitting was repeated three times.

Results. The goal of this study was to develop a qRTPCR assay to predict metastatic carcinoma tissue of origin. The experimental work consisted of two major parts. The first part included tissue-specific Marker candidates nomination, their validation on FFPE metastatic carcinoma tissues, and selection of ten Markers for the assay (FIG. 9A.). The second part included qRTPCR assay optimization followed by assay implementation on another set of FFPE metastatic carcinomas, building of a prediction algorithm, its cross-validation and validation on an independent sample set. (FIG. 9B).

Sample characteristics. RNA from a total of 700 frozen primary tissue samples was used for the gene expression profiling and tissue type specific gene identification. Samples included 545 primary carcinomas (29 lung, 13 pancreas, 315 breast, 128 colorectal, 38 prostate, 22 ovarian), 37 benign lesions (1 lung, 4 colorectal, 6 breast, 26 prostate) and 118 (36 lung, 5 pancreas, 36 colorectal, 14 breast, 3 prostate, 24 ovarian) normal tissues.

A total of 375 metastatic carcinomas of known origin (Stage III-IV) and 26 prostate primary adenocarcinoma samples were used in the study. The metastatic carcinomas originated from lung, pancreas, colorectal, ovarian, prostate as well as other cancers. The “other” sample category consisted of metastasis derived from tissues other than lung, pancreas, colon, breast, ovary and prostate. Patients' characteristics are summarized in Table 18. TABLE 18 Metastatic CUP Sample Set Total Number 401 48 Average Age 57.8 ± 11* 62.13 ± 11.7 Gender Female 241 20 Male 160 28 Tissue of Origin Lung 65 9 Pancreas 63 2 Colorectal 61 4 Breast 63 5 Ovarian 82 2 Prostate 27 2 Kidney 8 8 Stomach 7 0 Other** 25 5 Carcinoma of Unknown Primary 11 Histopathological Diagnosis Adenocarcinoma, moderately/well 306 27 differentiated Adenocarcinoma, poorly differentiated 49 4 Squamous cell carcinoma 16 5 Poorly differentiated carcinoma 16 10 Small cell carcinoma 3 Melanoma 5 Lymphoma 3 Hepatocellular carcinoma 2 Mesothelioma 1 Other*** 14 2 Metastatic Site Lymph Nodes 73 1 Brain 17 14 Lung 20 7 Liver 75 11 Pelvic region (ovary, bladder, fallopian 53 2 tubes) Abdomen (Omentum (omentum, mesentery, 91 5 colon, peritoneum) Other (skin, thyroid, chest wall, umbilicus) 44 8 Unknown 2 Primary (prostate) 26 *Age is unknown for 26 patients **esophagus, bladder, pleura, liver gallbladder, bile ducts, larynx, pharynx, Non-Hodgkin lymphoma ***small cell, mesothelioma, hepatocellular, melanoma, lymphoma

Samples were separated into two sets: the validation set (205 specimens) that was used to validate Marker candidates' tissue-specific differential expression and the training set (260 specimens) that was used for testing of the optimized one-step qRTPCR procedure and training of a prediction algorithm. The first set of 205 samples included 25 lung, 41 pancreas, 31 colorectal, 33 breast, 33 ovarian, 1 prostate, 23 other cancer metastasis and 18 prostate primary cancers. The second set consisted of 260 samples included 56 lung, 43 pancreas, 30 colorectal, 30 breast, 49 ovarian, 32 other cancer metastasis and 20 primary prostate cancers. Sixty-four specimens, including 16 lung, 21 pancreas, 15 other metastatic, and 12 prostate primary carcinomas were from the same patient in both sets.

The independent sample set obtained from Albany Medical College was comprised of 33 CUP specimens with a primary suggested for 22 of them, and 15 metastatic carcinomas of known origin. For CUPs having a suggested primary, a diagnosis was rendered based on morphological features, and/or results of testing with a panel of IHC Markers. Patient demographic, clinical and pathology characteristics are presented in Table 18.

Marker candidate selection. Analysis of gene expression profiles of 5 primary tissues types (lung, colon, breast, ovary, prostate) resulted in nomination of 13 tissue specific Marker candidates for qRTPCR testing. Top candidates have been identified in previous studies of cancers in situ. Argani et al. (2001); Backus et al. (2005); Cunha et al. (2005); Borgono et al. (2004); McCarthy et al. (2003); Hwang et al. (2004); Fleming et al. (2000); Nakamura et al. (2002); and Khoor et al. (1997). In addition to the analysis of the microarray data, two Markers were selected from the literature, including a complementary lung squamous cell carcinoma Marker DSG3 and the breast Marker PDEF. Backus et al. (2005). The microarray data confirmed the high sensitivity and specificity of these Markers.

A special approach was used to identify pancreas specific Markers. First, five pancreas Marker candidates were analyzed: prostate stem cell antigen (PSCA), serine proteinase inhibitor, clade A member 1 (SERPINA1), cytokeratin 7 (KRT7), matrix metalloprotease 11 (MMP11), and mucin 4 (MUC4) (Varadhachary et al. (2004); Argani et al. (2001); Jones et al. (2004); Prasad et al. (2005); and Moniaux et al. (2004)) using DNA microarrays and a panel of 13 pancreatic ductal adenocarcinomas, five normal pancreas tissues, and 98 samples from breast, colorectal, lung, and ovarian tumors. Only PSCA demonstrated moderate sensitivity (six out of thirteen or 46% of pancreatic tumors were detected) at a high specificity (91 out of 98 or 93% were correctly identified as not being of pancreatic origin). In contrast, KRT7, SERPINA1, MMP11, and MUC4 demonstrated sensitivities of 38%, 31%, 85%, and 31%, respectively, at specificities of 66%, 91%, 82%, and 81%, respectively. These data were in good agreement with qRTPCR performed on 27 metastases of pancreatic origin and 39 metastases of non-pancreatic origin for all Markers except for MMP11 which showed poorer sensitivity and specificity with qRTPCR and the metastases. In conclusion, the microarray data on snap frozen, primary tissue serves as a good indicator of the ability of the Marker to identify a FFPE metastasis as being pancreatic in origin using qRTPCR but that additional Markers may be useful for optimal performance.

Pancreatic ductal adenocarcinoma develops from ductal epithelial cells that comprise only a small percentage of all pancreatic cells (with acinar and islet cells comprising the majority) in the normal pancreas. Furthermore, pancreatic adenocarcinoma tissues contain a significant amount of adjacent normal tissue. Prasad et al. (2005); and Ishikawa et al. (2005). Because of this the candidate pancreas Markers were enriched for genes elevated in pancreas adenocarcinoma relative to normal pancreas cells. The first query method returned six probe sets: coagulation factor V (F5), a hypothetical protein FLJ22041 similar to FK506 binding proteins (FKBP10), beta 6 integrin (ITGB6), transglutaminase 2 (TGM2), heterogeneous nuclear ribonucleoprotein A0 (HNRP0), and BAX delta (BAX). The second query method (see Materials and Methods section for details) returned eight probe sets: F5, TGM2, paired-like homeodomain transcription factor 1 (PITX1), trio isoform mRNA (TRIO), mRNA for p73H (p73), an unknown protein for MGC:10264 (SCD), and two probe sets for claudin18.

A total of 23 tissue specific Marker candidates were selected for further RT-PCR validation on metastatic carcinoma FFPE tissues by qRT-PCR. Marker candidates were tested on 205 FFPE metastatic carcinomas, from lung, pancreas, colon, breast, ovary, prostate and prostate primary carcinomas. Table 19 provides the gene symbols of the tissue specific Markers selected for RT-PCR validation and also summarizes the results of testing performed with these Markers. TABLE 19 SEQ ID method Marker selection filters Tissue ID Micro Low exp corres Marker Tissue cross Marker type NOs array Lit met tissue redundancy reactivity adequate? Lung 1/59 X X X 60 X X X 61 X X X Pancreas 66 X X 67 X X 71 X X 72 X X 73 X 74 X 75 X 76 X Colon 4/85 X X X 77 X X 78 X X X 79 X X X Prostate 9/86 X X X 80 X X X Breast 63 X X X 81 X X X 64 X X Ovarian 82 X X X 83 X X X 65 X X X

Out of 23 tested Markers, thirteen were rejected based on their cross reactivity, low expression level in the corresponding metastatic tissues, or redundancy. Ten Markers were selected for the final version of assay. The lung Markers were human surfactant pulmonary-associated protein B (HUMPSPB), thyroid transcription factor 1 (TTF1), and desmoglein 3 (DSG3). The pancreas Markers were prostate stem cell antigen (PSCA) and coagulation factor V (F5), and the prostate Marker was kallikrein 3 (KLK3). The colorectal Marker was cadherin 17 (CDH17). Breast Markers were mammaglobin (MG) and prostate-derived Ets transcription factor (PDEF). The ovarian Marker was Wilms tumor 1 (WT1). Mean normalized relative expression values of selected Markers in different metastatic tissues are presented on FIG. 10.

Optimization of sample preparation and qRT-PCR using FFPE tissues. Next the RNA isolation and qRTPCR methods were optimized using fixed tissues before examining the performance of the Marker panel. First the effect of reducing the proteinase K incubation time from sixteen hours to 3 hours was analyzed. There was no effect on yield. However, some samples showed longer fragments of RNA when the shorter proteinase K step was used (FIG. 11A, B). For example, when RNA was isolated from a one-year-old block (C22), no difference was observed in the electropherograms. However, when RNA was isolated from a five-year-old block (C23), a larger fraction of higher molecular weight RNAs were observed, as assessed by the hump in the shoulder, when the shorter proteinase K digest was used. This trend generally held when other samples were processed, regardless of the organ of origin for the FFPE metastasis. In conclusion, shortening the proteinase K digestion time does not sacrifice RNA yields and may aid in isolating longer, less degraded RNA.

Next three different methods of reverse transcription were compared: reverse transcription with random hexamers followed by qPCR (two step), reverse transcription with a gene-specific primer followed by qPCR (two step), and a one-step qRTPCR using gene-specific primers. RNA was isolated from eleven metastases and compared Ct values across the three methods for β-actin, HUMSPB (FIG. 11C, D) and TTF. The results showed statistically significant differences (p<0.001) for all comparisons. For both genes, the reverse transcription with random hexamers followed by qPCR (two step reaction) gave the highest Ct values while the reverse transcription with a gene-specific primer followed by qPCR (two-step reaction) gave slightly (but statistically significant) lower Ct values than the corresponding 1 step reaction. However, the two-step RTPCR with gene-specific primers had a longer reverse transcription step. When HUMSPB Ct values were normalized to the corresponding β-actin value for each sample, there were no differences in the normalized Ct values across the three methods. In conclusion, optimization of the RTPCR reaction conditions can generate lower Ct values, which aids in analyzing older paraffin blocks (Cronin et al. (2004)), and a one step RTPCR reaction with gene-specific primers can generate Ct values comparable to those generated in the corresponding two step reaction.

Diagnostic performance ofoptimized qRTPCR assay. 12 qRTPCR reactions (10 Markers and 2 housekeeping genes) were performed on new set of 260 FFPE metastases. Twenty-one samples gave high Ct values for the housekeeping genes so only 239 were used in a heat map analysis. Analysis of the normalized Ct values in a heat map revealed the high specificity of the breast and prostate Markers, moderate specificity of the colon, lung, and ovarian, and somewhat lower specificity of the pancreas Markers (FIG. 12). Combining the normalized qRTPCR data with computational refinement improves performance of the Marker panel.

Using expression values, normalized to average of expression of two housekeeping genes, an algorithm to predict metastasis tissue of origin was developed by combining the normalized qRTPCR data with the algorithm and determined the accuracy of the qRTPCR assay by performing a leave-one-out-cross-validation test (LOOCV). For the six tissue types included in the assay, it was separately estimated that both the number of false-positive calls, when a sample was wrongly predicted as another tumor type included in thc assay (pancreas as colon, for example), and the number of times a sample was not predicted as those included in the assay tissue types (other). Results of the LOOCV are presented on Table 20. TABLE 20 Tissue of Origin Prediction Breast Colon Lung Ovary Pancreas Prostate Other Total Breast 22 0 2 1 1 0 0 Colon 1 27 3 2 4 0 4 Lung 1 2 45 2 3 0 5 Other 1 1 3 1 4 0 16 Ovary 5 0 0 43 0 0 1 Pancreas 0 0 3 0 31 0 6 Prostate 0 0 0 0 0 20 0 Total 30 30 56 49 43 20 32 260 # Correct 22 27 45 43 31 20 16 204 Accuracy 72.3 90.0 87.8 87.8 72.1 100.0 50.0 78.5

The tissue of origin was predicted correctly for 204 out of 260 tested samples with an overall accuracy of 78%. A significant proportion of the false positive calls were due to the Markers' cross-reactivity in histologically similar tissues. For example, three squamous cell metastatic carcinomas originated from pharynx, larynx and esophagus were wrongly predicted as lung due to DSG3 expression in these tissues. Positive expression of CDH17 in other than colon GI carcinomas, including stomach and pancreas, caused false classification of 4 out of 6 tested stomach and 3 out of 43 tested pancreatic cancer metastasis as colon.

In addition to a LOOCV test, the data was randomly split into 3 separate pairs of training and test sets. Each split contained approximately 50% of the samples from each class. At 50/50 splits in three separate pairs of training and test sets, assay overall classification accuracies were 77%, 71% and 75%, confirming assay performance stability.

Last, another independent set of 48 FFPE metastatic carcinomas that included metastatic carcinoma of known primary, CUP specimens with a tissue of origin diagnosis rendered by pathological evaluation including IHC, and CUP specimens that remained CUP after IHC testing were tested. The tissue of origin prediction accuracy was estimated separately for each category of samples. Table 21 summarizes the assay results. TABLE 21 Tested Correct Accuracy Known mets 15 11 73.3 Resolved CUP 22 17 77.3 Unresolved CUP 11

The tissue of origin prediction was, with only a few exceptions, consistent with the known primary or tissue of origin diagnosis assessed by clinical/pathological evaluation including IHC. Similar to the training set, the assay was not able to differentiate squamous cell carcinomas originating from different sources and falsely predicted them as lung.

The assay also made putative tissue of origin diagnoses for eight out of eleven samples which remained CUP after standard diagnostic tests. One of the CUP cases was especially interesting. A male patient with a history of prostate cancer was diagnosed with metastatic carcinoma in lung and pleura. Serum PSA tests and IHC with PSA antibodies on metastatic tissue were negative, so the pathologist's diagnosis was CUP with an inclination toward gastrointestinal tumors. The assay strongly (posterior probability 0.99) predicted the tissue of origin as colon.

Discussion. In this study, microarray-based expression profiling on primary tumors was used to identify candidate Markers for use with metastases. The fact that primary tumors can be used to discover tumor of origin Markers for metastases is consistent with several recent findings. For example, Weigelt and colleagues have shown that gene expression profiles of primary breast tumors are maintained in distant metastases. Weigelt et al. (2003). Backus and colleagues identified putative Markers for detecting breast cancer metastasis using a genome-wide gene expression analysis of breast and other tissues and demonstrated that mammaglobin and CK19 detected clinically actionable metastasis in breast sentinel lymph nodes with 90% sensitivity and 94% specificity. Backus et al. (2005).

During the development of the assay, selection was focused on six cancer types, including lung, pancreas and colon which are among the most prevalent in CUP (Ghosh et al. (2005); and Pavlidis et al. (2005)) and breast, ovarian and prostate for which treatment could be potentially most beneficial for patients. Ghosh et al. (2005). However, additional tissue types and Markers can be added to the panel as long as the overall accuracy of the assay is not compromised and, if applicable, the logistics of the RTPCR reactions are not encumbered.

The microarray-based studies with primary tissue confirmed the specificity and sensitivity of known Markers. As a result, the majority of tissue specific Markers have high specificity for the tissues studied here. A recent study found that, using IHC, PSCA is overexpressed in prostate cancer metastases. Lam et al. (2005). Dennis et al. (2002) also demonstrated that PSCA could be used as a tumor of origin Marker for pancreas and prostate. Strong expression of PSCA in some prostate tissues at the RNA level was present but, because due to inclusion of PSA in the assay, prostate and pancreatic cancers can now be segregated. A novel finding of this study was the use of F5 as a complementary (to PSCA) Marker for pancreatic tissue of origin. In both the microarray data set with primary tissue and the qRTPCR data set with FFPE metastases, F5 was found to complement PSCA.

Previous investigators have generated CUP assays using IHC (Brown et al. (1997); DeYoung et al. (2000); and Dennis et al. (2005a)) or microarrays. Su et al. (2001); Ramaswamy et al. (2001); and Bloom et al. (2004). More recently, SAGE has been coupled to a small qRTPCR Marker panel. Dennis et al. (2002); and Buckhaults et al. (2003). This study is the first to combine microarray-based expression profiling with a small panel of qRTPCR assays. The microarray studies with primary tissue identified some, but not all, of the same tissue of origin Markers as those identified previously by SAGE studies. This finding is not surprising given studies that have demonstrated that a modest agreement between SAGE- and DNA microarray-based profiling data exists and that the correlation improves for genes with higher expression levels. van Ruissen et al. (2005); and Kim et al. (2003). For example, Dennis and colleagues identified PSA, MG, PSCA, and HUMSPB while Buckhaults and coworkers (Buckhaults et al. (2003)) identified PDEF. Execution of the CUP assay is preferably by qRTPCR because it is a robust technology and may have performance advantages over IHC. Al-Mulla et al. (2005); and Haas et al. (2005). Further, as shown herein, the qRTPCR protocol has been improved through the use of gene-specific primers in a one-step reaction. This is the first demonstration of the use of gene-specific primers in a one-step qRTPCR reaction with FFPE tissue. Other investigators have either done a two-step qRTPCR (cDNA synthesis in one reaction followed by qPCR) or have used random hexamers or truncated gene-specific primers. Abrahamsen et al. (2003); Specht et al. (2001); Godfrey et al. (2000); Cronin et al. (2004); and Mikhitarian et al. (2004).

In summary, the 78% overall accuracy of the assay for six tissue types compares favorably to other studies. Brown et al. (1997); DeYoung et al. (2000); Dennis et al. (2005a); Su et al. (2001); Ramaswamy et al. (2001); and Bloom et al. (2004).

EXAMPLE 7

In this study ciassifier using gene marker portfolios were built by choosing from MVO and using this classifier to predict tissue origin and cancer status for five major cancer types including breast, colon, lung, ovarian and prostate. Three hundred and seventy eight primary cancer, 23 benign proliferative epithelial lesions and 103 normal snap-frozen human tissue specimens were analyzed by using Affymetrix human U133A GeneChip. Leukocyte samples were also analyzed in order to subtract gene expression potentially masked by co-expression in leukocyte background cells. A novel MVO-based bioinformatics method was developed to select gene marker portfolios for tissue of origin and cancer status. The data demonstrated that a panel of 26 genes could be used as a classifier to accurately predict the tissue of origin and cancer status among the 5 cancer types. Thus a multi-cancer classification method is obtainable by determining gene expression profiles of a reasonably small number of gene markers.

Table 22 shows the Markers identified for the tissue origins indicated. For gene descriptions see Table 31. TABLE 22 Tissue SEQ ID NO: Name Lung 59 SP-B 60 TTF1 61 DSG3 Pancreas 66 PSCA 67 F5 71 ITGB6 72 TGM2 84 HNRPA0 Colon 85 HPT1 77 FABP1 78 CDX1 79 GUCY2C Prostate 86 PSA 80 hKLK2 Breast 63 MGB1 81 PIP 64 PDEF Ovarian 82 HE4 83 PAX8 65 WT1

The sample set included a total of 299 metastatic colon, breast, pancreas, ovary, prostate, lung and other carcinomas and primary prostate cancer samples. QC based on histological evaluation, RNA yield and expression of control gene beta-actin was implemented. Other samples category included metastasis originated from stomach (5), kidney (6), cholangio/gallbladder (4), liver (2), head and neck (4), ileum (1) carcinomas and one mesothelioma. Tables 23 summarizes the results. TABLE 23 Histology RNA ACTB Tissue type Collected QC isolation QC Cut-off QC Lung 41 37 36 25 Pancreas 63 57 49 41 Colon 45 42 42 31 Breast 40 35 35 34 Ovarian 37 36 35 33 Prostate 27 27 25 19 Other 46 34 29 23 Total 299 268 251 205

Testing the above samples resulted in the narrowing of the Marker set to those in Table 24 with the results seen in Table 25. TABLE 24 Final Marker Table Lung surfactant-associated protein SP-B thyroid transcription factor 1 TTF1 desmoglein 3 DSG3 Pancreas prostate stem cell antigen PSCA coagulation factor 5 F5 Colon intestinal peptide-associated transporter HPT1 Prostate prostate-specific antigen PSA Breast Mammaglobin MGB Ets transcription factor PDEF Ovary Wilms tumor 1 WT1

TABLE 25 Cancer Samples # Marker Correct Sens % Wrong Spec % Lung 25/180 SP-B 13/25 52 0/180 100 TTF 12/25 48 1/180 99 DSG3  5/25 20 0/180 100 Pancreas 41/164 PSCA 24/41 59 6/164 96 F5  6/41 15 4/164 98 Colon 31/174 HPT1 22/31 71 2/174 99 Breast 33/172 MGB 23/33 70 3/172 98 PDEF 16/33 48 1/172 99 Prostate 19/186 PSA 19/19 100 0/186 100 PDEF 19/19 100 2/186 99 Ovarian 33/172 WT1 24/33 71 1/172 99 Total 205

The results showed that out of 205 paraffin embedded metastatic tumors; 166 samples (81%) had conclusive assay results, Table 26. TABLE 26 Candidate Correct Incorrect No Accuracy (%) Lung SP-B + 19 0 6 76 TFF + DSG3 Pancreas PSCA + F5 27 1 13 66 Colon HPT1 24 2 5 78 Prostate PSA 19 0 0 100 Breast MGB + PDEF 23 3 7 70 Ovarian WT1 23 2 8 70 Other 20 3 87 Overall 155 11 39 76

Of the false positive results, many false derived from histologically and embryologically similar tissues, Table 27. TABLE 27 Sample ID Diagnosis Predicted OV_26 Ovarian Breast Br_24 Breast Colon Br_37 Breast Colon CRC_25 Colon Ovarian Pn_59 Pancreas Colon Cont_27 Stomach pancreas Cont_34 Stomach Colon Cont_35 Stomach Colon Cont_43 Bile duct Pancreas Cont_44 Bile duct Pancreas Cong_25 Liver pancreas

The following parameters were considered for the model development:

Separate markers on female and male sets and calculate CUP probability separately for male and female patients. The male set included: SP_B, TTF1, DSG3, PSCA, F5, PSA, HPT1; the female set included: SP_B, TTF1, DSG3, PSCA, F5, HPT1, MGB, PDEF, WT1. Background expression was excluded from the assay results: Lung: SP_B, TTF1, DSG3; Ovary: WT1; and Colon: HPT1.

The CUP model was adjusted to the CUP prevalence (%): lung 23, pancreas 16, colorectal 9, breast 3, ovarian 4, prostate 2, other 43. The prevalence for breast and ovarian adjusted to 0% for male patients, and prostate adjusted to 0% for female patients.

The following steps were taken:

Place markers on similar scale.

Reduce number of variables from 12 to 8 by selecting minimum value from each tissue specific set.

Leave out 1 sample. Build model from remaining samples. Test left out sample. Repeat until 100% of samples are tested.

Randomly leave out ˜50% of samples (˜50% per tissue). Build model from remaining samples. Test −50% of samples. Repeat for 3 different random splits.

Classification accuracy was adjusted to cancer types prevalence

To produce the results summarized in Table 28 with the raw data shown in Table 29. TABLE 28 Breast Colon Lung Other Ovary Pancreas Prostate Overall Adjusted Correct 23 29 22 19 24 35 19 171 NoTest 3 2 2 2 3 0 12 Incorrect 7 0 1 4 7 3 0 22 Prevalence 0.03 0.09 0.23 0.43 0.04 0.16 0.02 Tested/total % 91 94 92 100 94 93 100 94 95 Correct/total % 70 94 88 83 73 85 100 89 89 NoTest % 9 6 8 n/a 6 7 0 6 5 Correct 23 25 19 20 20 24 19 150 NoTest % 7 6 5 10 15 0 43 Incorrect 3 0 1 3 3 2 0 12 Prevalence 0.03 0/09 0.23 0.43 0.04 0.16 0.02 Tested/total % 79 81 80 100 70 63 100 79 83 Correct/total % 70 81 76 87 61 59 100 73 76 Correct/tested % 88 100 95 87 87 92 100 93 91 NoTest % 21 19 20 n/a 30 37 0 21 17

TABLE 29 Sample ID Gender Origin BK Prediction BACTIN PBGD Ave CDH17 128 f breast lung 23.37 30.04 26.71 40.00 134 f breast uk breast 19.60 27.00 23.30 40.00 166 f breast uk breast 23.47 27.95 25.71 40.00 331 f breast ovary breast 25.12 31.40 28.26 40.00 356 f breast uk breast 28.59 33.89 31.24 40.00 163 f colon uk colon 24.69 30.34 27.52 29.39 184 m colon uk colon 22.47 28.63 25.55 26.22 339 f colon uk colon 28.35 34.29 31.32 33.76 346 m colon lung colon 23.15 28.77 25.96 26.36 363 m colon uk colon 24.46 30.62 27.54 26.20 101 m lung uk lung 24.68 28.79 26.74 40.00 106 m lung uk lung 22.05 27.50 24.78 40.00 110 m lung uk lung 29.19 32.32 30.76 40.00 112 m lung uk 22.48 27.79 25.14 40.00 199 f lung uk lung 21.21 27.07 24.14 35.65 200 m lung uk lung 22.16 26.94 24.55 40.00 313 m lung uk 24.76 30.05 27.41 38.40 323 m lung uk 23.82 30.24 27.03 32.43 325 m lung uk lung 22.09 27.97 25.03 40.00 335 m lung uk 24.89 29.73 27.31 40.00 347 m lung uk lung 23.40 29.08 26.24 40.00 374 m lung uk lung 22.50 28.23 25.37 40.00 385 f lung uk lung 21.65 26.44 24.05 37.05 114 f other lung other 24.80 30.56 27.68 40.00 129 m other lung other 21.49 28.25 24.87 39.47 179 f other uk other 23.97 30.45 27.21 40.00 194 m other uk other 25.28 32.47 28.88 40.00 302 f other colon 25.67 31.47 28.57 34.17 305 m other uk other 23.80 29.74 26.77 29.64 317 m other uk 25.90 30.62 28.26 40.00 333 f other uk other 22.45 28.82 25.64 30.54 334 m other uk other 22.14 29.20 25.67 31.79 342 f other uk 27.32 31.37 29.35 32.36 382 m other uk other 25.04 30.22 27.63 40.00 404 m other uk other 23.27 30.16 26.72 40.00 354 f ovary uk ovary 24.62 31.54 28.08 40.00 148 f ovary uk 23.55 29.88 26.72 40.00 417 f pancreas uk pancreas 23.42 29.46 26.44 28.28 136 m prostate lung prostate 22.37 26.95 24.66 40.00 407 m prostate lung prostate 28.20 31.87 30.04 40.00 116 f CUP uk lungSCC 21.66 27.31 24.49 28.95 123 m CUP lung colon 27.09 30.59 28.84 27.92 157 m CUP uk pancreas 26.81 31.94 29.38 40.00 177 m CUP uk pancreas 25.44 31.52 28.48 40.00 306 m CUP uk lung 23.15 28.38 25.77 37.30 360 m CUP uk other 21.14 27.43 24.29 33.97 372 f CUP uk ovary 23.16 29.12 26.14 40.00 187 f CUP uk colon 24.44 29.80 27.12 26.83 Sample ID DSG3 F5 HUMP KLK3 MG PDEF PSCA TTF1 WT1 128 37.78 35.74 22.19 40.00 40.00 30.36 29.96 29.39 34.85 134 31.27 30.83 40.00 40.00 29.51 25.07 24.67 40.00 34.13 166 40.00 26.66 40.00 28.20 24.78 25.19 30.69 40.00 35.32 331 40.00 40.00 40.00 40.00 22.26 26.01 40.00 40.00 40.00 356 34.01 40.00 40.00 40.00 35.73 33.19 30.72 40.00 40.00 163 40.00 26.52 40.00 40.00 40.00 37.72 40.00 40.00 36.17 184 33.26 28.76 40.00 40.00 40.00 34.07 33.44 40.00 31.64 339 40.00 40.00 40.00 40.00 40.00 35.99 40.00 40.00 40.00 346 40.00 32.64 20.89 40.00 40.00 32.47 40.00 26.75 30.58 363 31.84 29.98 34.44 40.00 40.00 30.45 35.00 40.00 30.35 101 40.00 39.34 21.57 40.00 40.00 28.21 27.47 40.00 35.76 106 40.00 32.24 23.68 40.00 40.00 25.79 25.02 26.42 37.27 110 40.00 40.00 21.21 40.00 40.00 32.77 32.43 30.70 36.13 112 37.05 37.38 36.08 40.00 40.00 37.12 36.04 40.00 37.45 199 25.56 31.23 40.00 40.00 28.94 32.19 27.95 32.14 31.60 200 24.53 33.69 40.00 40.00 40.00 36.67 38.34 38.61 33.55 313 40.00 40.00 40.00 40.00 40.00 40.00 40.00 40.00 35.11 323 31.82 33.81 40.00 40.00 40.00 33.60 28.12 40.00 31.87 325 26.84 34.88 38.61 40.00 38.04 34.29 27.31 39.21 31.23 335 29.62 38.00 40.00 40.00 40.00 39.23 40.00 31.12 32.12 347 26.72 37.21 40.00 40.00 40.00 36.10 30.76 40.00 39.44 374 40.00 38.76 21.38 40.00 37.26 26.56 38.26 24.86 36.60 385 40.00 34.51 19.89 40.00 40.00 27.36 40.00 23.72 37.09 114 40.00 28.16 21.51 40.00 40.00 35.76 37.85 28.19 37.21 129 40.00 28.86 20.65 40.00 40.00 32.98 40.00 28.14 31.11 179 40.00 29.79 40.00 40.00 40.00 40.00 40.00 40.00 32.64 194 40.00 28.90 40.00 40.00 40.00 40.00 40.00 34.75 35.41 302 40.00 40.00 40.00 40.00 40.00 30.55 32.47 40.00 38.20 305 40.00 34.06 40.00 40.00 40.00 31.82 40.00 40.00 40.00 317 40.00 27.75 40.00 40.00 40.00 31.89 33.06 40.00 35.12 333 40.00 37.01 40.00 40.00 40.00 37.85 40.00 40.00 40.00 334 40.00 36.27 40.00 40.00 40.00 34.69 40.00 40.00 40.00 342 40.00 29.24 40.00 40.00 40.00 32.89 40.00 40.00 38.18 382 40.00 36.13 40.00 40.00 40.00 38.30 40.00 40.00 34.91 404 39.36 34.75 40.00 40.00 40.00 39.02 40.00 40.00 34.24 354 40.00 34.90 40.00 40.00 40.00 36.62 40.00 40.00 29.71 148 40.00 30.60 38.84 40.00 40.00 32.12 31.76 40.00 38.59 417 38.96 29.05 37.01 40.00 40.00 30.15 30.23 40.00 30.69 136 40.00 29.47 23.69 21.38 40.00 24.70 24.28 30.89 31.16 407 40.00 40.00 27.70 25.98 40.00 27.65 40.00 39.13 38.76 116 27.86 31.06 40.00 40.00 30.28 33.49 29.31 40.00 38.11 123 36.01 40.00 40.00 40.00 40.00 40.00 40.00 40.00 36.65 157 40.00 26.82 40.00 40.00 40.00 36.68 40.00 40.00 40.00 177 40.00 27.15 40.00 40.00 40.00 39.67 40.00 40.00 34.71 306 40.00 34.94 19.71 40.00 40.00 30.81 40.00 25.45 39.28 360 36.98 32.72 40.00 40.00 40.00 27.75 40.00 40.00 40.00 372 40.00 34.07 40.00 40.00 40.00 32.93 40.00 40.00 25.28 187 35.91 26.32 30.55 40.00 40.00 40.00 40.00 29.75 40.00

EXAMPLE 8

Prospective Gene Signature Study of Metastatic Cancer of Unknown Primary Site CUP to Predict the Tissue of Origin

The specific aim of this study was to determine the ability of the 10-gene signature to predict tissue of origin of metastatic carcinoma in patients with carcinoma of unknown primary (CUP).

Primary objective: Confirm the feasibility of conducting gene analysis from core biopsy samples in consecutive patients with CUP.

Secondary objective: Correlate the results of the 10-gene signature RT-PCR assay with diagnostic work-up done at M.D. Anderson Cancer Center (MDACC).

Third objective: Correlate prevalence of 6 cancer types predicted by assay with the prevalence derived from the literature and MDACC experience.

The method described herein was used to perform a microarray gene expression analysis of 700 frozen primary carcinoma, and benign and normal specimens and identified gene marker candidates, specific for lung, pancreas, colon, breast, prostate and ovarian carcinomas. Gene marker candidates were tested by RT-PCR on 205 formalin-fixed, paraffin-embedded (FFPE) specimens of metastatic carcinoma (Stage III-IV) originated from lung, pancreas, colon, breast, ovary and prostate as well as metastasis originated from other cancer types for specificity control. Other metastatic cancer types included gastric, renal cell, hepatocellular, cholangio/gallbladder and head and neck carcinomas. Results allowed selecting of 10-gene signature that predicted tissue of origin of metastatic carcinoma and gave an overall accuracy of 76%. The average CV for repeated measurements in RT-PCR experiments is 1.5%, calculated based on 4 replicate date points. Beta-actin (ACTB) was used as housekeeping gene and its median expression was the similar in metastatic samples of different origin (CV=5.6%).

Specific aim for this study was to validate the ability of 10-gene signature to predict metastatic carcinoma tissue of origin in the CUP patients compared to comprehensive diagnostic workup.

Patient Eligibility

Patient must be at least 18 years old with a ECOG performance status of 0-2. Patients with diagnosis adenocarcinoma or poorly differentiated carcinoma diagnosis were accepted. Adenocarcinoma patient's group include well, moderate and poor differentiated tumors.

Patients have fulfilled the criteria for CUP: no primary detected after a complete evaluation which is defined as complete history and physical examination, detailed laboratory examination, imaging studies and symptom or sign directed invasive studies. Only untreated patients were allowed on the study.

If a patient has been treated with chemotherapy or radiation, participation in the study is allowed if prior (to treatment) tissue is available as archived blocks within 10 years time period

Patients provided written consent/authorization to participate in this study.

Study Design

Patients with diagnosis of CUP who have undergone a core needle or excision biopsy of the most accessible metastatic lesion were allowed on the study. Patients with FNA biopsy only were not eligible. The first 60 consecutive presenting patients who met the inclusion criteria and consent to the study were enrolled. If repeated biopsy is required at MDACC for diagnostic purposes for their treatment, additional tissue was obtained for the study if patient consented. All participants were registered on the protocol in the institutional Protocol Data Management System (PDMS).

Complete diagnostic work-up, including clinical and pathological assessments, was performed on all enrolled patients according MDACC standards. Pathology part of diagnostic work-up may have included immunohistochemistry (IHC) assays with markers including CK-7, CK-20, TTF-1 and other as deemed indicated by the pathologist. This is part of routine work up of all patients who present with CUP.

Tissue Sample Collection

Study included formalin-fixed paraffin embedded metastatic carcinoma specimens collected from CUP patients.

Six 10 μm sections were used for RNA isolation, smaller tissue specimens will require nine 10 μm sections. Histopathology diagnosis and tumor content were confirmed for each sample used for RNA isolation on an additional section stained with hematoxylin and eosin (HE). The tumor sample should have had a greater than 30% of tumor content in the HE section.

Clinical data were anonymously supplied to Veridex and include patient age, gender, tumor histology by light microscopy, tumor grade (differentiation), site of metastasis, date of specimen collection, description of the diagnostic workup performed for individual patient.

Tissue Processing and RT-PCR Experiments

Total RNA was extracted from each tissue sample using the protocol described above. Only samples that yielded more than 1 μm of total RNA out of standard amount of tissue were used for subsequent RT-PCR testing. Samples with less RNA yield were considered degraded and excluded from subsequent experiments. RNA integrity control based on housekeeping expression were implemented in order to exclude samples with degraded RNA, according the standard Veridex procedure.

RT-PCR assay that includes panel of 10 genes and 1-2 control genes was used for the analysis of the RNA samples. The reverse transcription and the PCR assay are completed using the protocols described above.

Relative expression value for each tested gene presented as ΔCt, which is equal to Ct of the target gene subtracted by Ct of the control genes, was calculated and used for the tissue of origin prediction.

Sample Size and Data Interpretation

A limited sample size of 60 patients were studied due to the exploratory nature of the pilot study. Up to the date, 22 patients have been tested. One patient samples failed to yield enough RNA for RT-PCR test and 3 failed to pass QC control assessed by RT-PCR with control genes. A total of 18 patients were used for determine probability of patient's metastatic lesion.

The statistical model was used to determine probability of metastatic carcinoma tissue of origin of following seven categories: lung, pancreas, colon, breast, prostate, ovarian and no test (other). For each sample, the probability for each category are calculated from a linear classification model. Assay results are summarized in Table 30.

The probability of a patient's metastatic lesion (with known primaries) coming from one of these 6 sites (colon, pancreas, lung, prostate, ovary, breast) is about 76%. This number is derived from literature given the incidence of various cancers and potential for spread and unpublished data generated at M.D. Anderson from tumor registry. For the tested samples, prevalence of 6 sites was 67% (12 out 18 tested samples), which very close consistent with previous observations. TABLE 30 Patient data ToO posterior probability (%) ID M/F prediction Breast Colon Lung LungSCC Other Ovary Pancreas prostate 1 M Other 0.00 0.00 0.81 0.00 98.68 0.00 0.51 0.00 4 F Colon 0.00 99.70 0.00 0.00 0.09 0.20 0.01 0.00 5 M Lung 0.00 33.29 52.27 0.01 13.30 0.00 1.13 0.00 6 F Colon 0.00 99.91 0.00 0.00 0.09 0.00 0.00 0.00 2 M Colon 0.00 93.19 0.01 0.00 2.90 0.00 3.90 0.00 10 F Other 0.02 2.04 0.03 0.03 61.43 1.12 35.34 0.00 16 F Colon 0.00 48.59 0.01 1.57 47.62 0.17 2.05 0.00 22 M LungSCC 0.00 8.85 0.01 71.69 11.84 0.00 7.62 0.00 23 M Colon 0.00 99.27 0.01 0.00 0.72 0.00 0.00 0.00 24 F Colon 0.00 90.59 0.00 0.00 2.36 0.00 7.04 0.00 26 F Lung 0.00 0.00 99.93 0.00 0.06 0.00 0.01 0.00 17 M Other 0.00 0.07 0.02 0.09 94.06 0.00 5.77 0.00 19 F Other 0.02 0.11 0.04 0.22 76.36 23.24 0.01 0.00 21 F Pancreas 0.00 6.97 0.00 0.00 2.37 8.43 82.23 0.00 27 F Other 0.00 0.04 0.04 0.59 99.06 0.14 0.13 0.00 11 M Other 0.00 0.23 0.07 0.09 99.52 0.00 0.09 0.00 32 F Ovary 0.00 0.01 0.00 0.00 7.23 92.63 0.13 0.00 34 M LungSCC 0.00 0.03 0.00 65.64 7.96 0.00 26.38 0.00 3 F ctr failure 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8 M ctr failure 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20 F ctr failure 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, the descriptions and examples should not be construed as limiting the scope of the invention. TABLE 31 Name SEQ ID NOs Accession Description CDH17 62 NM_004063 Cadherin 17 CDX1 78 NM_001804 Homeo box transcription factor 1 DSG3 61/3 NM_001944 Desmoglein 3 F5 67/6 NM_000130 Coagulation factor V FABP1 71 NM_001443 Fatty acid binding protein 1, liver GUCY2C 79 NM_004963 Guanylate cyclase 2C HE4 82 NM_006103 Putative ovarian carcinoma marker KLK2 80 BC005196 Kallikrein 2, prostatic HNRPA0 84 NM_006805 Heterogeneous nuclear ribonucleoprotein A0 HPT1 85/4 U07969 Intestinal peptide-associated transporter ITGB6 71 NM_000888 Integrin, beta 6 KLK3 68 NM_001648 Kallikrein 3 MGB1 63/7 NM_002411 Mammaglobin 1 PAX8 83 BC001060 Paired box gene 8 PBGD 70 NM_000190 Hydroxymethylbilane synthase PDEF 64/8 NM_012391 Domain containing Ets transcription factor PIP 81 NM_002652 Prolactin-induced protein PSA 86/9 U17040 Prostate specific antigen precursor PSCA 66/5 NM_005672 Prostate stem cell antigen SP-B 59/1 NM_198843 Pulmonary surfactant-associated protein B TGM2 72 NM_004613 Transglutaminase 2 TTF1 60/2 NM_003317 Similar to thyroid transcription factor 1 WT1  65/10 NM_024426 Wilms tumor 1 β-actin 69 NM_001101 β-actin KRT6F 87 L42612 keratin 6 isoform K6f p73H 88 AB010153 p53-related protein SFTPC 89 NM_003018 surfactant, pulmonary-associated protein C KLK10 90 NM_002776 Kallikrein 10 CLDN18 91 NM_016369 Claudin 18 TR10 92 BD280579 Tumor necrosis factor receptor B305D 93 B726 94 GABA-pi 95 BC109105 gamma-aminobutyric acid A receptor, pi StAR 96 NM_01007243 steroidogenic acute regulator EMX2 97 NM_004098 empty spiracles homolog 2 (Drosophila) NGEP 98 AY617079 NGEP long variant NPY 99 NM_000905 Neuropeptide Y SERPINA1 100  NM_000295 serpin peptidase inhibitor, clade A member 1 KRT7 101  NM_005556 Keratin 7 MMP11 102  NM_005940 matrix metallopeptidase 11 (stromelysin 3) MUC4 103  NM_018406 Mucin 4 cell-surface associated FLJ22041 104  AK025694 BAX 105  NM_138763 BCL2-assoc X protein transcript variant Δ PITX1 106  NM_002653 paired-like homeodomain trans factor 1 MGC: 10264 107  BC005807 stearoyl-CoA desaturase (Δ-9-desaturase) References

U.S. patent application Ser. No. publications and Pat. Nos. 5242974 5700637 20030194733 5350840 5786148 20030198970 5384261 6004755 20030215803 5405783 6136182 20030215835 5412087 6218114 20030219760 5424186 6218122 20030219767 5429807 6225051 20030232350 5436327 6232073 20030235820 5445934 6261766 20040005563 5472672 6271002 20040009154 5527681 6339148 20040009489 5529756 20010029020 20040018969 5532128 20020055627 20040029114 5545531 20020068288 20040076955 5554501 20020168647 20040126808 5556752 20030044859 20040146862 5561071 20030087818 20040219572 5571639 20030104448 20040219575 5593839 20030124128 20050037010 5599695 20030124579 20050059008 5624711 20030138793 20060094035 5658734 20030190656

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1. A method of identifying origin of a metastasis of unknown origin comprising the steps of a. obtaining a sample containing metastatic cells; b. measuring Biomarkers associated with at least two different carcinomas; c. combining the data from the Biomarkers into an algorithm where the algorithm i. normalizes the Biomarkers against a reference; and ii. imposes a cut-off which optimizes sensitivity and specificity of each Biomarker, weights the prevalence of the carcinomas and selects a tissue of origin; d. determining origin based on highest probability determined by the algorithm or determining that the carcinoma is not derived from a particular set of carcinomas; and e. optionally measuring Biomarkers specific for one or more additional different carcinoma, and repeating steps c) and d) for the additional Biomarkers.
 2. The method of claim 1 wherein the Marker genes are selected from at least one from a group corresponding to: i. SP-B, TTF, DSG3, KRT6F, p73H, or SFTPC; ii. F5, PSCA, ITGB6, KLK10, CLDN18, TR10 or FKBP10; or iii. CDH17, CDX1 or FABP1.
 3. The method of claim 2 wherein the Marker genes are SP-B, TTF, DSG3, KRT6F, p73H, or SFTPC.
 4. The method according to claim 3 wherein the Marker genes are SP-B, TTF and DSG3.
 5. The method according to claim 4 wherein the Marker genes further comprise or are replaced by KRT6F, p73H, and/or SFTPC.
 6. The method of claim 2 wherein the Marker genes are F5, PSCA, ITGB6, KLK10, CLDN18, TR10 or FKBP10.
 7. The method of claim 6 wherein the Marker genes are F5 and PSCA.
 8. The method of claim 7 wherein the Marker genes further comprise or are replaced by ITGB6, KLK10, CLDN18, TR10 and/or FKBP10.
 9. The method of claim 1 wherein the Marker genes are CDH17, CDX1 or FABP1.
 10. The method of claim 9 wherein the Marker gene is CDH
 17. 11. The method of claim 10 wherein the Marker gene further comprises or are replaced by CDX1 and/or FABP1.
 12. The method of one of claims 1-11 wherein gene expression is measured using at least one of SEQ ID NOs: 11-58.
 13. The method of claim 2 wherein the Marker genes are further selected from a gender specific Marker selected from at least one of i. in the case of a male patient KLK3, KLK2, NGEP or NPY; or ii. in the case of a female patient PDEF, MGB, PIP, B305D, B726 or GABA-Pi; and/or WT1, PAX8, STAR or EMX2.
 14. The method of claim 13 wherein the Marker gene is KLK2.
 15. The method of claim 14 wherein the Marker gene is KLK3.
 16. The method of claim 15 wherein the Marker gene further comprises or are replaced by NGEP and/or NPY.
 17. The method of claim 13 wherein the Marker genes are PDEF, MGB, PIP, B305D, B726 or GABA-Pi.
 18. The method of claim 17 wherein the Marker genes are PDEF and MGB.
 19. The method of claim 18 wherein the Marker genes further comprise or are replaced by PIP, B305D, B726 or GABA-Pi.
 20. The method of claim 13 wherein the Marker genes are WT1, PAX8, STAR or EMX2.
 21. The method of claim 20 wherein the Marker gene is WT1.
 22. The method of claim 21 wherein the Marker gene further comprises or is replaced by PAX8, STAR or EMX2.
 23. The method of one of claims 13-22 wherein gene expression is measured using at least one of SEQ ID NOs: 11-58.
 24. The method of claim 1 or 2 comprising further obtaining additional clinical information including the site of metastasis to determine the origin of the carcinoma.
 25. A method of obtaining optimal biomarker sets for carcinomas comprising the steps of using metastases of know origin, determining Biomarkers therefor and comparing the Biomarkers to Biomarkers of metastases of unknown origin.
 26. A method of providing direction of therapy by determining the origin of a metastasis of unknown origin according to one of claims 1-3 and identifying the appropriate treatment therefor.
 27. A method of providing a prognosis by determining the origin of a metastasis of unknown origin according to one of claims 1-3 and identifying the corresponding prognosis therefor.
 28. A method of finding Biomarkers comprising determining the expression level of a Marker gene in a particular metastasis, measuring a Biomarker for the Marker gene to determine expression thereof, analyzing the expression of the Marker gene according to claim 1 and determining if the Marker gene is effectively specific for the tumor of origin.
 29. A composition comprising at least one isolated sequence selected from SEQ ID NOs: 11-58.
 30. A kit for conducting an assay according to one of claims 1-3 comprising: Biomarker detection reagents.
 31. A microarray or gene chip for performing the method of one of claims 1-3.
 32. A diagnostic/prognostic portfolio comprising isolated nucleic acid sequences, their complements, or portions thereof of a combination of genes according to one of claims 2-11, or 13-22 where the combination is sufficient to measure or characterize gene expression in a biological sample having metastatic cells relative to cells from different carcinomas or normal tissue.
 33. A method according to one of claims 2-11, or 13-22 further comprising measuring expression of at least one gene constitutively expressed in the sample. 